
import numpy as np
import pandas as pd
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
from pandas.core.common import SettingWithCopyWarning

import warnings
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
warnings.simplefilter(action="ignore", category=UserWarning)

import random
import datetime
import time
import matplotlib.pyplot as plt
%matplotlib inline

from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader, ConcatDataset
import torch.optim as optim


# Initialise the random state
#num = random.randint(1, 500)
num = 58
torch.manual_seed(num)
np.random.seed(num)
print(f"The generated random seed is {num}") #347

The generated random seed is 58


path = "/Users/niharawarawita/Desktop/MSc Project/Data and code/complete_dataset_3subwindows.csv"
df = pd.read_csv(path)
df.head(10)


# Remove enjoyment as we are only considering physical properties
print(df.shape)
physical_df = df[df.property_name!='enjoyment']
physical_df.shape

(1512, 191)

(1260, 191)


# Obtain a df of features
features_df = physical_df.iloc[:,11:]
features_df.head()


physical_df.head()


path_emg_0 = "/Users/niharawarawita/Desktop/MSc Project/Data and code/emg_0_15subwindows.csv"
emg_0_15 = pd.read_csv(path_emg_0)
emg_0_15.head(10)


path_emg_1 = "/Users/niharawarawita/Desktop/MSc Project/Data and code/emg_1_15subwindows.csv"
emg_1_15 = pd.read_csv(path_emg_1)
emg_1_15.head(10)


# Remove enjoyment as we are only considering physical properties
emg_0_15 = emg_0_15[emg_0_15.property_name!='enjoyment']
emg_1_15 = emg_1_15[emg_1_15.property_name!='enjoyment']
#emg_1_15


# Obtain a df of features
features_emg_0 = emg_0_15.iloc[:,11:]

features_emg_1 = emg_1_15.iloc[:,11:]
features_emg_1.head()


normalised_features_df = features_df.copy()
print(normalised_features_df.shape)

(1260, 180)


# create scaler
scaler = MinMaxScaler(feature_range=(-1,1)) # As this is the range of the activation function - tanh

# fit scaler and apply transform
normalised_features_df[normalised_features_df.columns] = scaler.fit_transform(features_df[features_df.columns])
normalised_features_df.shape

(1260, 180)


df_info = physical_df.iloc[:, :11]
df_info.head()


normalised_df = pd.concat([df_info,normalised_features_df], axis=1)
normalised_df.shape

(1260, 191)


normalised_features_emg_0 = features_emg_0.copy()
normalised_features_emg_1 = features_emg_1.copy()


# create scaler
scaler = MinMaxScaler(feature_range=(-1,1)) # As this is the range of the activation function - tanh

# fit scaler and apply transform
normalised_features_emg_0[normalised_features_emg_0.columns] = scaler.fit_transform(features_emg_0[features_emg_0.columns])
normalised_features_emg_1[normalised_features_emg_1.columns] = scaler.fit_transform(features_emg_1[features_emg_1.columns])


df_info_0 = features_emg_0.iloc[:, :11]
df_info_1 = features_emg_1.iloc[:, :11]


normalised_emg_0 = pd.concat([df_info_0, normalised_features_emg_0], axis=1)
normalised_emg_1 = pd.concat([df_info_1, normalised_features_emg_1], axis=1)


normalised_emg_1.shape

(190889, 16)


def create_X_2d(df, features_starting_idx):
    
    X_2d = df.iloc[:,features_starting_idx:].values
    
    X_tensor_2d = torch.Tensor(X_2d)    
    return X_tensor_2d


def create_y_train_for_2d_X(df, predicting_feature = 'property_id', output_as_tensor='Yes'):
    # CreatE an instance of a one-hot-encoder
    encoder = OneHotEncoder(handle_unknown='ignore')

    # Perform one-hot encoding on the specified column 
    encoder_df = pd.DataFrame(encoder.fit_transform(df[[predicting_feature]]).toarray())
    
    # Convert to a numpy array
    y_train = encoder_df.to_numpy()
    
    if output_as_tensor == 'Yes':
        # Convert to a tensor
        y_train = torch.Tensor(y_train)

    return y_train


def create_y_test_for_2d_X(df, predicting_feature = 'property_id'):   
    y_test = df[predicting_feature].values
    if predicting_feature == 'property_id':
        y_test = y_test - 3
    elif predicting_feature == 'rating_level_num':
        y_test = y_test - 1
    
    y_test_tensor = torch.Tensor(y_test)    
    y_test_tensor = y_test_tensor.type(torch.LongTensor)
    
    return y_test_tensor


def create_X_3d(df, features_starting_idx):
    dim1 = df.interaction_id.nunique()
    #num_timestamps_per_interaction = df.groupby('interaction_id')['sub_window_num'].nunique()
    dim2 = df.sub_window_num.nunique()
    dim3 = df.iloc[:,features_starting_idx:].shape[1]
        
    X = np.zeros((dim1, dim2, dim3)) 

    itr_id_lst = df.interaction_id.unique().tolist()

    for i in range(len(itr_id_lst)):
        itr_id = itr_id_lst[i]
        itr_id_df = df[df.interaction_id==itr_id]  
        
        for j in range(itr_id_df.shape[0]):
            vals_arr = itr_id_df.iloc[j,features_starting_idx:].values
            X[i,j] = vals_arr
    
    X_tensor = torch.Tensor(X)    
    return X_tensor


def create_y_train_for_3d_X(df, predicting_feature = 'property_id'):
    # Create a dataset with only the required columns
    df2 = df[['participant_id', 'clothes_id', 'property_id', 'rating_level_num']]

    # Remove duplicates
    df2.drop_duplicates(keep='first', inplace=True)

    # Reset the indexes
    df2.reset_index(drop=True, inplace=True) 
    
    ## Create y train
    # CreatE an instance of a one-hot-encoder
    encoder = OneHotEncoder(handle_unknown='ignore')

    # Perform one-hot encoding on the specified column 
    encoder_df = pd.DataFrame(encoder.fit_transform(df2[[predicting_feature]]).toarray())
    
    # Convert to a numpy array
    y_train = encoder_df.to_numpy()
    
    # Convert to a tensor
    y_train = torch.Tensor(y_train)
  
    return y_train


def create_y_test_for_3d_X(df, predicting_feature = 'property_id'):
    # Create a dataset with only the required columns
    df2 = df[['participant_id', 'clothes_id', 'property_id', 'rating_level_num']]

    # Remove duplicates
    df2.drop_duplicates(keep='first', inplace=True)

    # Reset the indexes
    df2.reset_index(drop=True, inplace=True) 
    
    y_test = df2[predicting_feature].values
    if predicting_feature == 'property_id':
        y_test = y_test - 3
    elif predicting_feature == 'rating_level_num':
        y_test = y_test - 1
    
    y_test = torch.Tensor(y_test)    
    y_test = y_test.type(torch.LongTensor)
    
    return y_test


class Linear_all_features_properties(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(180, 20)
        self.fc2 = nn.Linear(20, 10)
        self.fc3 = nn.Linear(10, 5)        
           
    def forward(self, x): #, x2
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x

class Linear_all_features_ratings(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(185, 20)
        self.fc2 = nn.Linear(20, 10)
        self.fc3 = nn.Linear(10, 3)        

    def forward(self, x): #, x2
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x


def find_best_model_for_2d_X(train_dataloader, val_dataloader, learning_rate, num_epochs, model):

    # Model
    train_model = model

    # Loss and Optimiser
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = optim.SGD(train_model.parameters(), lr=learning_rate, momentum=0.7)

    best_avg_loss = np.inf
    best_model = None
    #best_model_epoch_num = np.inf
    train_loss_lst = []
    val_loss_lst = []
    avg_loss_lst = []

    for epoch in range(num_epochs):  # loop over the dataset multiple times

        #Set the model in training mode
        train_model.train()

        # Initialise the total training and validation loss
        epoch_train_loss = 0
        epoch_val_loss = 0
        avg_loss = 0
        #count_train = 0
        #count_val = 0

        #running_loss = 0.0
        for i, train_data in enumerate(train_dataloader, 0):
            #print(len(train_data))
            #count_train += 1
            # get the inputs; data is a list of [input1, input2, label]
            train_input1, train_labels = train_data #train_input2, 

            #train_labels = train_labels.type(torch.LongTensor)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            train_preds = train_model(train_input1) #, train_input2
            #print(train_labels)#
            #print(train_preds)
            #print(train_labels)
            train_loss = criterion(train_preds, train_labels)
            train_loss.backward()
            optimizer.step()

            # Update training loss
            epoch_train_loss += train_loss.item()

        # Switch off auto grad for evaluation
        with torch.no_grad():

            # Set the model in evaluation mode
            train_model.eval()

            for j, val_data in enumerate(val_dataloader, 0):
               # count_val += 1
               # print(len(val_data))
            # get the inputs; data is a list of [input1, input2, label]
                val_input1, val_labels = val_data # val_input2, 

                #val_labels = val_labels.type(torch.LongTensor)

                val_preds = train_model(val_input1) #, val_input2

                # Update validation loss
                val_loss = criterion(val_preds, val_labels)

                epoch_val_loss += val_loss.item()
        #print(len(train_dataloader), len(val_dataloader))       
        avg_training_loss = epoch_train_loss / len(train_dataloader) #count_train
        avg_validation_loss = epoch_val_loss / len(val_dataloader) #count_val
        avg_loss = (avg_training_loss + avg_validation_loss) / 2
        
        train_loss_lst.append(avg_training_loss)
        val_loss_lst.append(avg_validation_loss)
        avg_loss_lst.append(avg_loss)
         
        #print(f'epoch {epoch+1}: train loss = {round(avg_training_loss,2)}, val loss = {round(avg_validation_loss,2)}, average loss = {round(avg_loss,2)}')

        if avg_loss < best_avg_loss:
            best_avg_loss = avg_loss
            best_model = train_model.state_dict()

    return best_avg_loss, best_model, train_loss_lst, val_loss_lst, avg_loss_lst   #, avg_loss_lst,


# Original
def Linear_LOP0CV(data, model, num_folds=5, predicting_feature='property_id', learning_rate=0.01, num_epochs=10, random_state=num): #, num_inner_folds=5
    # Set fixed random number seed
    torch.manual_seed(num)
    
   # elif predicting_feature == 'rating_level_num':
    #weighted_f1_lst = [] 
    total_conf_mat = 0
    micro_f1_lst = []
    #macro_f1_lst = []
    acc_lst = []
    if predicting_feature == 'property_id':
        macro_f1_lst = []
    elif predicting_feature == 'rating_level_num':
        weighted_f1_lst = []    



    #lst = [6,24]
    
    for participant in sorted(data.participant_id.unique()): #lst: #:
        print(f'LEAVING PARTICIPANT {participant} OUT:')
        
        # Split the data into training and testing
        training_data = data[data.participant_id != participant] 
        testing_data = data[data.participant_id == participant] 

        # Data preparation
        X_train = create_X_2d(training_data, 11)       
        y_train = create_y_train_for_2d_X(training_data, predicting_feature = predicting_feature, output_as_tensor='Yes')
        X_test = create_X_2d(testing_data, 11) 
        y_test = create_y_test_for_2d_X(testing_data, predicting_feature = predicting_feature)
        
        #print(X_train.shape)
        #print(y_train.shape)
        #print(X_test.shape)
        #print(y_test.shape)
        #print('------------')
        
        if predicting_feature == 'rating_level_num':
            X_train_add = create_y_train_for_2d_X(training_data, predicting_feature = 'property_id', output_as_tensor='Yes')
            #X_train_add = torch.Tensor(X_train_add)    
            #print(type(X_train_add))
            #print(type(X_train))
            X_train = torch.cat((X_train_add, X_train), 1)
            #X_train = pd.concat([X_train_add, X_train], axis=1)
            X_test_add = create_y_train_for_2d_X(testing_data, predicting_feature = 'property_id', output_as_tensor='Yes')
            #X_test_add = torch.Tensor(X_test_add)   
            X_test = torch.cat((X_test_add, X_test), 1)

        
        #print(X_train.shape)
        #print(y_train)
        #print(X_test.shape)
        #print(y_test)

        # Create the datasets and dataloaders
        train_dataset = TensorDataset(X_train, y_train) 
        
        test_dataset = TensorDataset(X_test, y_test)
        test_dataloader = torch.utils.data.DataLoader(test_dataset, shuffle=True, batch_size=90) # num_workers=2,       
            
        # Configure the cross-validation procedure
        cv_inner = KFold(n_splits=num_folds, shuffle=True, random_state=num)
                
        min_avg_loss_subject = np.inf
        best_model_subject = None
        
        best_train_loss_lst = None
        best_val_loss_lst = None
        best_avg_loss_lst = None
        best_fold = np.inf

        for fold, (train_ids, val_ids) in enumerate(cv_inner.split(train_dataset)):
            #print(f'FOLD {fold+1}:')
           # print(train_ids)
            #print(val_ids)
                       
            # Sample elements randomly from a given list of ids, no replacement.
            train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids) # The ids are the same for both
            val_subsampler = torch.utils.data.SubsetRandomSampler(val_ids)

            # Define data loaders for training and testing data in this fold
            train_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=train_subsampler) #, batch_size=100
            val_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=val_subsampler) #, batch_size=20        

            avg_loss_fold, best_model_fold, fold_train_loss_lst, fold_val_loss_lst, fold_avg_loss_lst = find_best_model_for_2d_X(train_dataloader, val_dataloader, learning_rate, num_epochs, model)
                           
            if avg_loss_fold < min_avg_loss_subject:
                min_avg_loss_subject = avg_loss_fold
                best_model_subject = best_model_fold
                best_train_loss_lst = fold_train_loss_lst
                best_val_loss_lst = fold_val_loss_lst
                best_avg_loss_lst = fold_avg_loss_lst
                best_fold = fold+1

        fig = plt.figure(f"{participant}")                  
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_train_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_val_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_avg_loss_lst)
        plt.title(f"Loss when participant {participant} is left out (lr={learning_rate}, num_epochs ={num_epochs})")
        plt.legend(['train loss', 'val loss', 'avg_loss'])

        # save trained model 
        name = 'model_participant_'+str(participant)+'.pt'
        torch.save(best_model_subject, name)
        print(f'The model for participant {participant} has been saved')
        
        test_model = model
        test_model.load_state_dict(torch.load(name))

        dataiter = iter(test_dataloader) 
        test_input1, test_labels = dataiter.next() 
    
        test_preds = test_model(test_input1) 
        
        test_preds_np = test_preds.detach().numpy()
        test_predicted_np = np.argmax(test_preds_np, axis = 1)
        
        test_labels_np = test_labels.numpy()       
               
        if predicting_feature == 'property_id':
            conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0, 1, 2, 3, 4])
            macro_f1_score = f1_score(test_labels_np, test_predicted_np, average='macro') 
            macro_f1_lst.append(macro_f1_score) 
        elif predicting_feature == 'rating_level_num':
            conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0,1,2])
            weighted_f1_score = f1_score(test_labels_np, test_predicted_np, average='weighted') 
            weighted_f1_lst.append(weighted_f1_score)
            
        total_conf_mat += conf_mat
        micro_f1_score = f1_score(test_labels_np, test_predicted_np, average='micro')  
        micro_f1_lst.append(micro_f1_score)
        acc = accuracy_score(test_labels_np, test_predicted_np)
        acc_lst.append(acc)


        print(f"Leaving participant {participant} out")
        print("(1) Confusion matrix:\n", conf_mat)
        print(f"(2) micro F1 score = {round(micro_f1_score,2)}") 
        if predicting_feature == 'property_id':
            print(f"(3) Macro F1 score = {round(macro_f1_score,2)}")
        elif predicting_feature == 'rating_level_num':
            print(f"(3) Weighted F1 score = {round(weighted_f1_score,2)}")            
        print(f"(4) Percentage Classification accuracy = {round(acc*100,2)}%")
        
        total_conf_mat += conf_mat 
        micro_f1_lst.append(micro_f1_score)
        acc_lst.append(acc)
        print('--------------------------------')
        
    avg_micro_f1_score = sum(micro_f1_lst) / len(micro_f1_lst)
    if predicting_feature == 'property_id':
        avg_macro_f1_score = sum(macro_f1_lst) / len(macro_f1_lst)  
    elif predicting_feature == 'rating_level_num':
        avg_weighted_f1_score = sum(weighted_f1_lst) / len(weighted_f1_lst)             
    avg_acc = sum(acc_lst) / len(acc_lst)   
    
    print(f'Using Leave One Participant Out CV (LOPOCV):') 
    print("(1) Confusion matrix:\n", total_conf_mat)
    print(f"(2) Average micro F1 score = {round(avg_micro_f1_score,2)}")
    if predicting_feature == 'property_id':
        print(f"(3) Average macro F1 score = {round(avg_macro_f1_score,2)}")
    elif predicting_feature == 'rating_level_num':
        print(f"(3) Average weighted F1 score = {round(avg_weighted_f1_score,2)}")      
    print(f"(4) Average Percentage Classification accuracy = {round(avg_acc*100,2)}%")


Linear_LOP0CV(normalised_df, model=Linear_all_features_properties(), num_folds=5, predicting_feature='property_id', learning_rate=0.005, num_epochs=150, random_state=num)

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[17  1  0  0  0]
 [10  7  0  0  1]
 [18  0  0  0  0]
 [ 3 13  0  2  0]
 [ 5 12  0  1  0]]
(2) micro F1 score = 0.29
(3) Macro F1 score = 0.19
(4) Percentage Classification accuracy = 28.89%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[ 6  5  1  4  2]
 [ 1 16  0  0  1]
 [ 0  3 15  0  0]
 [ 0  0  0 17  1]
 [ 2  7  0  5  4]]
(2) micro F1 score = 0.64
(3) Macro F1 score = 0.61
(4) Percentage Classification accuracy = 64.44%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[17  0  1  0  0]
 [ 0  1  4  5  8]
 [ 0  0  2 10  6]
 [ 0  0  1 16  1]
 [ 0  6  2  3  7]]
(2) micro F1 score = 0.48
(3) Macro F1 score = 0.43
(4) Percentage Classification accuracy = 47.78%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[14  1  3  0  0]
 [ 1  9  3  2  3]
 [ 0  3 12  2  1]
 [ 0  2  1 15  0]
 [ 0  3  5  4  6]]
(2) micro F1 score = 0.62
(3) Macro F1 score = 0.62
(4) Percentage Classification accuracy = 62.22%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[ 8  2  7  0  1]
 [ 0  1  0  0 17]
 [ 8  0  5  0  5]
 [ 0  0  0 12  6]
 [ 0  0  0  0 18]]
(2) micro F1 score = 0.49
(3) Macro F1 score = 0.45
(4) Percentage Classification accuracy = 48.89%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[11  5  0  1  1]
 [ 2 16  0  0  0]
 [ 0 17  0  0  1]
 [ 2 10  0  2  4]
 [ 1 10  0  1  6]]
(2) micro F1 score = 0.39
(3) Macro F1 score = 0.33
(4) Percentage Classification accuracy = 38.89%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[ 1 14  3  0  0]
 [ 0 15  0  1  2]
 [ 1 14  3  0  0]
 [ 1 10  0  5  2]
 [ 0  5  3  1  9]]
(2) micro F1 score = 0.37
(3) Macro F1 score = 0.34
(4) Percentage Classification accuracy = 36.67%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[ 8  0  0 10  0]
 [ 0  2  1 12  3]
 [ 1  1 11  5  0]
 [ 1  0  3 13  1]
 [ 0  0  0 13  5]]
(2) micro F1 score = 0.43
(3) Macro F1 score = 0.43
(4) Percentage Classification accuracy = 43.33%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[17  0  1  0  0]
 [ 9  1  5  3  0]
 [11  0  3  3  1]
 [ 1  1  0 16  0]
 [12  0  3  3  0]]
(2) micro F1 score = 0.41
(3) Macro F1 score = 0.31
(4) Percentage Classification accuracy = 41.11%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[ 8  6  0  0  4]
 [ 2 12  0  0  4]
 [ 0  2  0  0 16]
 [ 1  4  0  0 13]
 [ 4  8  0  0  6]]
(2) micro F1 score = 0.29
(3) Macro F1 score = 0.23
(4) Percentage Classification accuracy = 28.89%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[ 8  4  1  5  0]
 [ 5  6  1  4  2]
 [ 4  0  9  5  0]
 [ 1  0  0 17  0]
 [ 4  2  5  6  1]]
(2) micro F1 score = 0.46
(3) Macro F1 score = 0.41
(4) Percentage Classification accuracy = 45.56%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[ 0  0  0  0 18]
 [ 0  0  0  3 15]
 [ 0  1  0  4 13]
 [ 0  0  0 14  4]
 [ 0  0  0  3 15]]
(2) micro F1 score = 0.32
(3) Macro F1 score = 0.21
(4) Percentage Classification accuracy = 32.22%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[11  7  0  0  0]
 [ 3 14  0  0  1]
 [ 1 14  0  2  1]
 [ 0 14  0  4  0]
 [ 8  8  0  1  1]]
(2) micro F1 score = 0.33
(3) Macro F1 score = 0.27
(4) Percentage Classification accuracy = 33.33%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[ 5 12  0  1  0]
 [ 0 17  0  1  0]
 [ 2  8  4  4  0]
 [ 0  5  0 13  0]
 [ 1 17  0  0  0]]
(2) micro F1 score = 0.43
(3) Macro F1 score = 0.38
(4) Percentage Classification accuracy = 43.33%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[262 114  34  42  52]
 [ 66 234  28  62 114]
 [ 92 126 128  70  88]
 [ 20 118  10 292  64]
 [ 74 156  36  82 156]]
(2) Average micro F1 score = 0.43
(3) Average macro F1 score = 0.37
(4) Average Percentage Classification accuracy = 42.54%


Linear_LOP0CV(normalised_df, model=Linear_all_features_properties(), num_folds=5, predicting_feature='property_id', learning_rate=0.005, num_epochs=150, random_state=num)

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[17  1  0  0  0]
 [10  6  2  0  0]
 [18  0  0  0  0]
 [ 4 11  2  0  1]
 [ 3 12  3  0  0]]
(2) micro F1 score = 0.26
(3) Macro F1 score = 0.15
(4) Percentage Classification accuracy = 25.56%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[ 9  1  1  6  1]
 [ 0  3  2  2 11]
 [ 6  3  9  0  0]
 [ 0  0  0 17  1]
 [ 5  0  0  3 10]]
(2) micro F1 score = 0.53
(3) Macro F1 score = 0.51
(4) Percentage Classification accuracy = 53.33%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[10  6  0  0  2]
 [ 0 14  0  0  4]
 [ 0  7  0  0 11]
 [ 0  0  0 14  4]
 [ 0  8  0  0 10]]
(2) micro F1 score = 0.53
(3) Macro F1 score = 0.51
(4) Percentage Classification accuracy = 53.33%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[13  0  5  0  0]
 [ 0  4  9  3  2]
 [ 1  1 10  3  3]
 [ 0  1  1 14  2]
 [ 0  0  5  3 10]]
(2) micro F1 score = 0.57
(3) Macro F1 score = 0.56
(4) Percentage Classification accuracy = 56.67%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[15  0  3  0  0]
 [ 0 13  0  0  5]
 [ 7  1  9  0  1]
 [ 0  0  0 18  0]
 [ 0  0  1  0 17]]
(2) micro F1 score = 0.8
(3) Macro F1 score = 0.79
(4) Percentage Classification accuracy = 80.0%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[ 2  6  6  1  3]
 [ 0  6  1  4  7]
 [ 0  8  4  2  4]
 [ 0  0  0 12  6]
 [ 0  1  0  3 14]]
(2) micro F1 score = 0.42
(3) Macro F1 score = 0.38
(4) Percentage Classification accuracy = 42.22%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[ 0  1 13  1  3]
 [ 0 10  3  2  3]
 [ 0  2 15  0  1]
 [ 0  3  1  8  6]
 [ 0  0  0  1 17]]
(2) micro F1 score = 0.56
(3) Macro F1 score = 0.49
(4) Percentage Classification accuracy = 55.56%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[14  3  0  0  1]
 [ 1 12  0  1  4]
 [ 5  2 11  0  0]
 [ 1  3  1  9  4]
 [ 0  2  0  0 16]]
(2) micro F1 score = 0.69
(3) Macro F1 score = 0.69
(4) Percentage Classification accuracy = 68.89%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[ 0 13  4  1  0]
 [ 0 10  3  5  0]
 [ 0  5 12  1  0]
 [ 0  0  2 16  0]
 [ 1 10  3  4  0]]
(2) micro F1 score = 0.42
(3) Macro F1 score = 0.33
(4) Percentage Classification accuracy = 42.22%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[14  0  1  3  0]
 [ 4  2  0  2 10]
 [ 1  0 11  4  2]
 [ 5  0  1 10  2]
 [ 7  1  1  2  7]]
(2) micro F1 score = 0.49
(3) Macro F1 score = 0.46
(4) Percentage Classification accuracy = 48.89%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[ 0  2  4  5  7]
 [ 0  4  1  7  6]
 [ 0  4  2  7  5]
 [ 0  0  1 17  0]
 [ 0  1  0 13  4]]
(2) micro F1 score = 0.3
(3) Macro F1 score = 0.23
(4) Percentage Classification accuracy = 30.0%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[15  1  1  1  0]
 [ 1  4 10  2  1]
 [ 2  0 14  1  1]
 [ 3  1  3 10  1]
 [ 8  1  5  2  2]]
(2) micro F1 score = 0.5
(3) Macro F1 score = 0.45
(4) Percentage Classification accuracy = 50.0%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[9 9 0 0 0]
 [0 9 1 1 7]
 [0 2 4 9 3]
 [0 1 2 8 7]
 [4 4 2 1 7]]
(2) micro F1 score = 0.41
(3) Macro F1 score = 0.41
(4) Percentage Classification accuracy = 41.11%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[16  1  0  1  0]
 [ 5  5  3  4  1]
 [ 1  2 12  3  0]
 [ 0  0  0 17  1]
 [ 6  3  3  4  2]]
(2) micro F1 score = 0.58
(3) Macro F1 score = 0.52
(4) Percentage Classification accuracy = 57.78%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[268  88  76  38  34]
 [ 42 204  70  66 122]
 [ 82  74 226  60  62]
 [ 26  40  28 340  70]
 [ 68  86  46  72 232]]
(2) Average micro F1 score = 0.5
(3) Average macro F1 score = 0.46
(4) Average Percentage Classification accuracy = 50.4%


Linear_LOP0CV(normalised_df, model=Linear_all_features_ratings(), num_folds=5, predicting_feature='rating_level_num', learning_rate=0.005, num_epochs=150, random_state=num)
#rating_level_num

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[ 9 21  0]
 [18 27  0]
 [ 3 12  0]]
(2) micro F1 score = 0.4
(3) Weighted F1 score = 0.36
(4) Percentage Classification accuracy = 40.0%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[25 14  0]
 [21 30  0]
 [ 0  0  0]]
(2) micro F1 score = 0.61
(3) Weighted F1 score = 0.61
(4) Percentage Classification accuracy = 61.11%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[32  4  0]
 [16 17  6]
 [ 1  5  9]]
(2) micro F1 score = 0.64
(3) Weighted F1 score = 0.63
(4) Percentage Classification accuracy = 64.44%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[13 11  0]
 [ 1 56  0]
 [ 0  9  0]]
(2) micro F1 score = 0.77
(3) Weighted F1 score = 0.72
(4) Percentage Classification accuracy = 76.67%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[ 8 10  0]
 [ 0 45  0]
 [ 0 25  2]]
(2) micro F1 score = 0.61
(3) Weighted F1 score = 0.52
(4) Percentage Classification accuracy = 61.11%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[ 6  7  5]
 [ 2 35  8]
 [ 1 20  6]]
(2) micro F1 score = 0.52
(3) Weighted F1 score = 0.49
(4) Percentage Classification accuracy = 52.22%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[ 9  6  0]
 [ 0 48  0]
 [ 0  9 18]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.83
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[ 8 16  3]
 [ 2 46  0]
 [ 1  9  5]]
(2) micro F1 score = 0.66
(3) Weighted F1 score = 0.61
(4) Percentage Classification accuracy = 65.56%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[ 0 13  5]
 [ 0 46  2]
 [ 0  0 24]]
(2) micro F1 score = 0.78
(3) Weighted F1 score = 0.69
(4) Percentage Classification accuracy = 77.78%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[14 13  0]
 [ 5 46  0]
 [ 0  8  4]]
(2) micro F1 score = 0.71
(3) Weighted F1 score = 0.69
(4) Percentage Classification accuracy = 71.11%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[ 0  1  8]
 [ 0  7  8]
 [ 0  4 62]]
(2) micro F1 score = 0.77
(3) Weighted F1 score = 0.72
(4) Percentage Classification accuracy = 76.67%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[ 3  6  3]
 [ 0 41  1]
 [ 0  9 27]]
(2) micro F1 score = 0.79
(3) Weighted F1 score = 0.77
(4) Percentage Classification accuracy = 78.89%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[ 0  5  4]
 [ 0 57  0]
 [ 0  7 17]]
(2) micro F1 score = 0.82
(3) Weighted F1 score = 0.77
(4) Percentage Classification accuracy = 82.22%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[ 0 16  2]
 [ 0 48  3]
 [ 0  3 18]]
(2) micro F1 score = 0.73
(3) Weighted F1 score = 0.65
(4) Percentage Classification accuracy = 73.33%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[ 254  286   60]
 [ 130 1098   56]
 [  12  240  384]]
(2) Average micro F1 score = 0.69
(3) Average weighted F1 score = 0.65
(4) Average Percentage Classification accuracy = 68.89%


complete_emg = normalised_df.iloc[:,:59]


class Linear_emg_features_properties(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(48, 25)
        self.fc2 = nn.Linear(25, 10)
        self.fc3 = nn.Linear(10, 5)        
           
    def forward(self, x): #, x2
        #print(x.shape)
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x

class Linear_emg_features_ratings(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(48+5, 25)
        self.fc2 = nn.Linear(25, 10)
        self.fc3 = nn.Linear(10, 3)        

    def forward(self, x): #, x2
        #print(x.shape)
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x


Linear_LOP0CV(complete_emg, model=Linear_emg_features_properties(), num_folds=5, predicting_feature='property_id', learning_rate=0.005, num_epochs=150, random_state=num)

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[16  0  2  0  0]
 [12  0  1  0  5]
 [18  0  0  0  0]
 [ 7  0  1  2  8]
 [ 7  0  2  0  9]]
(2) micro F1 score = 0.3
(3) Macro F1 score = 0.21
(4) Percentage Classification accuracy = 30.0%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[ 0  8  1  7  2]
 [ 0  8  0 10  0]
 [ 0  1 17  0  0]
 [ 0  0  0 17  1]
 [ 0  3  1 10  4]]
(2) micro F1 score = 0.51
(3) Macro F1 score = 0.44
(4) Percentage Classification accuracy = 51.11%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[17  0  1  0  0]
 [ 0 17  0  0  1]
 [ 1  1 15  0  1]
 [ 0  3  0 14  1]
 [ 0  6  1  0 11]]
(2) micro F1 score = 0.82
(3) Macro F1 score = 0.82
(4) Percentage Classification accuracy = 82.22%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[12  5  0  0  1]
 [ 0 13  0  4  1]
 [ 1  6  9  2  0]
 [ 0  2  1 15  0]
 [ 1  1  0  3 13]]
(2) micro F1 score = 0.69
(3) Macro F1 score = 0.69
(4) Percentage Classification accuracy = 68.89%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[16  0  2  0  0]
 [ 0 16  2  0  0]
 [ 5  3  9  1  0]
 [ 0  0  0 17  1]
 [ 0  2  1  0 15]]
(2) micro F1 score = 0.81
(3) Macro F1 score = 0.81
(4) Percentage Classification accuracy = 81.11%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[17  0  0  1  0]
 [ 2 13  1  0  2]
 [ 2  0 14  0  2]
 [ 1  2  1 12  2]
 [ 0  4  0  1 13]]
(2) micro F1 score = 0.77
(3) Macro F1 score = 0.77
(4) Percentage Classification accuracy = 76.67%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[17  0  0  0  1]
 [ 4 10  1  0  3]
 [ 4  2 12  0  0]
 [ 4  0  0  8  6]
 [ 2  0  0  1 15]]
(2) micro F1 score = 0.69
(3) Macro F1 score = 0.69
(4) Percentage Classification accuracy = 68.89%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[16  1  1  0  0]
 [ 1 15  2  0  0]
 [ 1  5 12  0  0]
 [ 2  3  0 11  2]
 [ 3  2  1  0 12]]
(2) micro F1 score = 0.73
(3) Macro F1 score = 0.74
(4) Percentage Classification accuracy = 73.33%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[16  0  2  0  0]
 [ 3  5  9  1  0]
 [ 4  0 12  2  0]
 [ 0  0  4 14  0]
 [11  1  0  0  6]]
(2) micro F1 score = 0.59
(3) Macro F1 score = 0.57
(4) Percentage Classification accuracy = 58.89%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[16  1  1  0  0]
 [ 5  9  2  2  0]
 [ 2  2 12  2  0]
 [ 4  0  5  9  0]
 [ 5  8  1  2  2]]
(2) micro F1 score = 0.53
(3) Macro F1 score = 0.49
(4) Percentage Classification accuracy = 53.33%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[11  2  1  1  3]
 [ 1 14  1  2  0]
 [ 0  4  9  2  3]
 [ 0  2  3 12  1]
 [ 3  3  5  3  4]]
(2) micro F1 score = 0.56
(3) Macro F1 score = 0.54
(4) Percentage Classification accuracy = 55.56%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[16  0  0  2  0]
 [ 1 16  1  0  0]
 [ 2  4 10  2  0]
 [ 0  2  1 15  0]
 [ 8  6  0  4  0]]
(2) micro F1 score = 0.63
(3) Macro F1 score = 0.56
(4) Percentage Classification accuracy = 63.33%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[16  0  2  0  0]
 [ 1 15  2  0  0]
 [ 0  2 12  4  0]
 [ 1  1  1 15  0]
 [ 9  1  4  2  2]]
(2) micro F1 score = 0.67
(3) Macro F1 score = 0.62
(4) Percentage Classification accuracy = 66.67%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[13  2  2  0  1]
 [ 0 13  1  4  0]
 [ 3  1 13  1  0]
 [ 0  0  1 16  1]
 [ 2  6  4  2  4]]
(2) micro F1 score = 0.66
(3) Macro F1 score = 0.63
(4) Percentage Classification accuracy = 65.56%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[398  38  30  22  16]
 [ 60 328  46  46  24]
 [ 86  62 312  32  12]
 [ 38  30  36 354  46]
 [102  86  40  56 220]]
(2) Average micro F1 score = 0.64
(3) Average macro F1 score = 0.61
(4) Average Percentage Classification accuracy = 63.97%


Linear_LOP0CV(complete_emg, model=Linear_emg_features_ratings(), num_folds=5, predicting_feature='rating_level_num', learning_rate=0.0001, num_epochs=150, random_state=num)

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[ 0 29  1]
 [ 0 45  0]
 [ 0 15  0]]
(2) micro F1 score = 0.5
(3) Weighted F1 score = 0.34
(4) Percentage Classification accuracy = 50.0%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[ 0 39  0]
 [ 0 51  0]
 [ 0  0  0]]
(2) micro F1 score = 0.57
(3) Weighted F1 score = 0.41
(4) Percentage Classification accuracy = 56.67%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[12 24  0]
 [ 6 32  1]
 [ 4  8  3]]
(2) micro F1 score = 0.52
(3) Weighted F1 score = 0.49
(4) Percentage Classification accuracy = 52.22%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[ 7 17  0]
 [ 7 50  0]
 [ 0  9  0]]
(2) micro F1 score = 0.63
(3) Weighted F1 score = 0.57
(4) Percentage Classification accuracy = 63.33%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[ 8 10  0]
 [ 1 44  0]
 [ 0 27  0]]
(2) micro F1 score = 0.58
(3) Weighted F1 score = 0.47
(4) Percentage Classification accuracy = 57.78%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[ 2 16  0]
 [ 0 45  0]
 [ 1 26  0]]
(2) micro F1 score = 0.52
(3) Weighted F1 score = 0.38
(4) Percentage Classification accuracy = 52.22%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[11  4  0]
 [ 0 44  4]
 [ 0 10 17]]
(2) micro F1 score = 0.8
(3) Weighted F1 score = 0.8
(4) Percentage Classification accuracy = 80.0%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[ 7 17  3]
 [ 4 42  2]
 [ 0  8  7]]
(2) micro F1 score = 0.62
(3) Weighted F1 score = 0.59
(4) Percentage Classification accuracy = 62.22%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[ 2 14  2]
 [ 0 47  1]
 [ 0 12 12]]
(2) micro F1 score = 0.68
(3) Weighted F1 score = 0.62
(4) Percentage Classification accuracy = 67.78%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[17 10  0]
 [ 8 43  0]
 [ 6  6  0]]
(2) micro F1 score = 0.67
(3) Weighted F1 score = 0.62
(4) Percentage Classification accuracy = 66.67%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[ 3  1  5]
 [ 0  1 14]
 [ 0  0 66]]
(2) micro F1 score = 0.78
(3) Weighted F1 score = 0.71
(4) Percentage Classification accuracy = 77.78%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[ 0  4  8]
 [ 0 27 15]
 [ 0  9 27]]
(2) micro F1 score = 0.6
(3) Weighted F1 score = 0.56
(4) Percentage Classification accuracy = 60.0%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[ 0  3  6]
 [ 0 57  0]
 [ 0  3 21]]
(2) micro F1 score = 0.87
(3) Weighted F1 score = 0.82
(4) Percentage Classification accuracy = 86.67%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[ 0 15  3]
 [ 1 50  0]
 [ 0 12  9]]
(2) micro F1 score = 0.66
(3) Weighted F1 score = 0.57
(4) Percentage Classification accuracy = 65.56%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[ 138  406   56]
 [  54 1156   74]
 [  22  290  324]]
(2) Average micro F1 score = 0.64
(3) Average weighted F1 score = 0.57
(4) Average Percentage Classification accuracy = 64.21%


# Obtain features for each hand
hand1_emg = normalised_df.iloc[:,35:59]

hand0_acc = normalised_df.iloc[:,59:86]
hand1_acc = normalised_df.iloc[:,86:113]

hand0_qua = normalised_df.iloc[:,113:152]
hand1_qua = normalised_df.iloc[:,152:]

# Combine the data to ceate a df for each hand
emg_0 = normalised_df.iloc[:,:35]
hand_0 = pd.concat([emg_0, hand0_acc, hand0_qua], axis=1)
hand_1 = pd.concat([df_info, hand1_emg, hand1_acc, hand1_qua], axis=1)


class LSTM_all_features_properties(nn.Module):
    def __init__(self):
        super().__init__()
        self.rnn = nn.LSTM(90, 40, 1, batch_first=True)
        self.fc1 = nn.Linear(3 * 40 * 2, 20)
        self.fc2 = nn.Linear(20, 10)
        self.fc3 = nn.Linear(10, 5)
          
    def forward(self, x1, x2): #, x2
        x1, (hn, cn) = self.rnn(x1) #, (self.h0, self.c0)
        x1 = F.tanh(x1)
        x2, (hm, cm) = self.rnn(x2) # (self.h0, self.c0)
        x2 = F.tanh(x2)
        
        x = torch.cat((x1, x2), 2)
        
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x
    
class LSTM_all_features_ratings(nn.Module):
    def __init__(self):
        super().__init__()
        self.rnn = nn.LSTM(90+5, 40, 1, batch_first=True)
        self.fc1 = nn.Linear(3 * 40 * 2, 20)
        self.fc2 = nn.Linear(20, 10)
        self.fc3 = nn.Linear(10, 3)
          
    def forward(self, x1, x2): #, x2
        x1, (hn, cn) = self.rnn(x1) #, (self.h0, self.c0)
        x1 = F.tanh(x1)
        x2, (hm, cm) = self.rnn(x2) # (self.h0, self.c0)
        x2 = F.tanh(x2)
        
        x = torch.cat((x1, x2), 2)
        
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x


def find_best_model_for_3d_X(train_dataloader, val_dataloader, learning_rate, num_epochs, model):

    # Model
    train_model = model

    # Loss and Optimiser
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = optim.SGD(train_model.parameters(), lr=learning_rate, momentum=0.7)

    best_avg_loss = np.inf
    best_model = None
    #best_model_epoch_num = np.inf
    train_loss_lst = []
    val_loss_lst = []
    avg_loss_lst = []

    for epoch in range(num_epochs):  # loop over the dataset multiple times

        #Set the model in training mode
        train_model.train()

        # Initialise the total training and validation loss
        epoch_train_loss = 0
        epoch_val_loss = 0
        avg_loss = 0

        #running_loss = 0.0
        for i, train_data in enumerate(train_dataloader, 0):
            #print(len(train_data))

            # get the inputs; data is a list of [input1, input2, label]
            train_input1, train_input2, train_labels = train_data #train_input2, 

            #train_labels = train_labels.type(torch.LongTensor)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            train_preds = train_model(train_input1, train_input2)  
            #print(train_labels)#

            train_loss = criterion(train_preds, train_labels)
            train_loss.backward()
            optimizer.step()

            # Update training loss
            epoch_train_loss += train_loss.item()

        # Switch off auto grad for evaluation
        with torch.no_grad():

            # Set the model in evaluation mode
            train_model.eval()

            for j, val_data in enumerate(val_dataloader, 0):
               # print(len(val_data))
            # get the inputs; data is a list of [input1, input2, label]
                val_input1, val_input2, val_labels = val_data # val_input2, 

                #val_labels = val_labels.type(torch.LongTensor)

                val_preds = train_model(val_input1, val_input2) #, val_input2

                # Update validation loss
                val_loss = criterion(val_preds, val_labels)

                epoch_val_loss += val_loss.item()
     
        avg_training_loss = epoch_train_loss / len(train_dataloader) #count_train
        avg_validation_loss = epoch_val_loss / len(val_dataloader) #count_val
        avg_loss = (avg_training_loss + avg_validation_loss) / 2
        
        train_loss_lst.append(avg_training_loss)
        val_loss_lst.append(avg_validation_loss)
        avg_loss_lst.append(avg_loss)
         
        #print(f'epoch {epoch+1}: train loss = {round(avg_training_loss,3)}, val loss = {round(avg_validation_loss,3)}, average loss = {round(avg_loss,3)}')

        if avg_loss < best_avg_loss:
            best_avg_loss = avg_loss
            best_model = train_model.state_dict()

    return best_avg_loss, best_model, train_loss_lst, val_loss_lst, avg_loss_lst   #, avg_loss_lst,


# Original
def LSTM_LOP0CV(data_0, data_1, model, num_folds=5, predicting_feature='property_id', learning_rate=0.01, num_epochs=10, random_state=num): #, num_inner_folds=5
    # Set fixed random number seed
    torch.manual_seed(num)
        
    total_conf_mat = 0
    micro_f1_lst = []
    acc_lst = []
    if predicting_feature == 'property_id':
        macro_f1_lst = []
    elif predicting_feature == 'rating_level_num':
        weighted_f1_lst = [] 
        
        data0_add = create_y_train_for_2d_X(data_0, predicting_feature = 'property_id', output_as_tensor='No')
        data0_add_pd = pd.DataFrame(data0_add, columns = ['smoothness','thickness','warmth', 'flexibility', 'softness'])
        data_0 = pd.concat([data_0.reset_index(drop=True), data0_add_pd.reset_index(drop=True)], axis=1)
        data1_add = create_y_train_for_2d_X(data_1, predicting_feature = 'property_id', output_as_tensor='No')
        data1_add_pd = pd.DataFrame(data1_add, columns = ['smoothness','thickness','warmth', 'flexibility', 'softness'])
        data_1 = pd.concat([data_1.reset_index(drop=True), data1_add_pd.reset_index(drop=True)], axis=1)
    
    for participant in sorted(data_0.participant_id.unique()): # # #:lst: #
        print(f'LEAVING PARTICIPANT {participant} OUT:')
        
        # Split the data into training and testing
        training_data_0 = data_0[data_0.participant_id != participant] 
        training_data_1 = data_1[data_1.participant_id != participant] 
        testing_data_0 = data_0[data_0.participant_id == participant] 
        testing_data_1 = data_1[data_1.participant_id == participant] 

        # Data preparation
        X_train_0 = create_X_3d(training_data_0, 11)
        X_train_1 = create_X_3d(training_data_1, 11) 
        X_test_0 = create_X_3d(testing_data_0, 11) 
        X_test_1 = create_X_3d(testing_data_1, 11)           
        y_train = create_y_train_for_3d_X(training_data_0, predicting_feature = predicting_feature)
        y_test = create_y_test_for_3d_X(testing_data_0, predicting_feature = predicting_feature)        
 

        #print(X_train_0.shape)
        #print(X_train_1.shape)
        #print(y_train.shape)
        #print(X_test_0.shape)
        #print(X_test_1.shape)
        #print(y_test.shape)
 
        # Create the datasets and dataloaders
        train_dataset = TensorDataset(X_train_0, X_train_1, y_train) 
        
        test_dataset = TensorDataset(X_test_0, X_test_1, y_test)
        test_dataloader = torch.utils.data.DataLoader(test_dataset, shuffle=True, batch_size=30) # num_workers=2,       
            
        # Configure the cross-validation procedure
        cv_inner = KFold(n_splits=num_folds, shuffle=True, random_state=num)
                
        min_avg_loss_subject = np.inf
        best_model_subject = None
        
        best_train_loss_lst = None
        best_val_loss_lst = None
        best_avg_loss_lst = None
        best_fold = np.inf

        for fold, (train_ids, val_ids) in enumerate(cv_inner.split(train_dataset)):
            #print(f'FOLD {fold+1}:')
                       
            # Sample elements randomly from a given list of ids, no replacement.
            train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids) # The ids are the same for both
            val_subsampler = torch.utils.data.SubsetRandomSampler(val_ids)

            # Define data loaders for training and testing data in this fold
            train_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=train_subsampler) #, batch_size=100
            val_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=val_subsampler) #, batch_size=20        

            avg_loss_fold, best_model_fold, fold_train_loss_lst, fold_val_loss_lst, fold_avg_loss_lst = find_best_model_for_3d_X(train_dataloader, val_dataloader, learning_rate, num_epochs, model)
                           
            if avg_loss_fold < min_avg_loss_subject:
                min_avg_loss_subject = avg_loss_fold
                best_model_subject = best_model_fold
                best_train_loss_lst = fold_train_loss_lst
                best_val_loss_lst = fold_val_loss_lst
                best_avg_loss_lst = fold_avg_loss_lst
                best_fold = fold+1

        fig = plt.figure(f"{participant}")                  
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_train_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_val_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_avg_loss_lst)
        plt.title(f"Loss when participant {participant} is left out for fold {best_fold} (lr={learning_rate}, num_epochs ={num_epochs})")
        plt.legend(['train loss', 'val loss', 'avg_loss'])

        # save trained model 
        name = 'model_participant_'+str(participant)+'.pt'
        torch.save(best_model_subject, name)
        print(f'The model for participant {participant} has been saved')
        
        test_model = model
        test_model.load_state_dict(torch.load(name))

        dataiter = iter(test_dataloader) 
        test_input1, test_input2, test_labels = dataiter.next() 
    
        test_preds = test_model(test_input1, test_input2) 
        
        test_preds_np = test_preds.detach().numpy()
        test_predicted_np = np.argmax(test_preds_np, axis = 1)
        
        test_labels_np = test_labels.numpy()    
        
        if predicting_feature == 'property_id':
            conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0, 1, 2, 3, 4])
            macro_f1_score = f1_score(test_labels_np, test_predicted_np, average='macro') 
            macro_f1_lst.append(macro_f1_score) 
        elif predicting_feature == 'rating_level_num':
            conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0,1,2])
            weighted_f1_score = f1_score(test_labels_np, test_predicted_np, average='weighted') 
            weighted_f1_lst.append(weighted_f1_score)
            
        total_conf_mat += conf_mat
        micro_f1_score = f1_score(test_labels_np, test_predicted_np, average='micro')  
        micro_f1_lst.append(micro_f1_score)
        acc = accuracy_score(test_labels_np, test_predicted_np)
        acc_lst.append(acc)


        print(f"Leaving participant {participant} out")
        print("(1) Confusion matrix:\n", conf_mat)
        print(f"(2) micro F1 score = {round(micro_f1_score,2)}")
        if predicting_feature == 'property_id':
            print(f"(3) Macro F1 score = {round(macro_f1_score,2)}")
        elif predicting_feature == 'rating_level_num':
            print(f"(3) Weighted F1 score = {round(weighted_f1_score,2)}")            
        print(f"(4) Percentage Classification accuracy = {round(acc*100,2)}%")
        
        total_conf_mat += conf_mat 
        micro_f1_lst.append(micro_f1_score)
        acc_lst.append(acc)
        print('--------------------------------')
        
    avg_micro_f1_score = sum(micro_f1_lst) / len(micro_f1_lst)
    if predicting_feature == 'property_id':
        avg_macro_f1_score = sum(macro_f1_lst) / len(macro_f1_lst)  
    elif predicting_feature == 'rating_level_num':
        avg_weighted_f1_score = sum(weighted_f1_lst) / len(weighted_f1_lst)             
    avg_acc = sum(acc_lst) / len(acc_lst)   
    
    print(f'Using Leave One Participant Out CV (LOPOCV):') 
    print("(1) Confusion matrix:\n", total_conf_mat)
    print(f"(2) Average micro F1 score = {round(avg_micro_f1_score,2)}")
    if predicting_feature == 'property_id':
        print(f"(3) Average macro F1 score = {round(avg_macro_f1_score,2)}")
    elif predicting_feature == 'rating_level_num':
        print(f"(3) Average weighted F1 score = {round(avg_weighted_f1_score,2)}")      
    print(f"(4) Average Percentage Classification accuracy = {round(avg_acc*100,2)}%")


LSTM_LOP0CV(hand_0, hand_1, model=LSTM_all_features_properties(), num_folds=5, predicting_feature='property_id', learning_rate=0.085, num_epochs=50, random_state=num) #, num_inner_folds=5

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[0 0 0 6 0]
 [0 0 0 6 0]
 [1 0 0 5 0]
 [0 0 0 6 0]
 [0 0 0 6 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[0 6 0 0 0]
 [0 6 0 0 0]
 [0 6 0 0 0]
 [0 6 0 0 0]
 [0 6 0 0 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[1 0 0 0 5]
 [0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]]
(2) micro F1 score = 0.23
(3) Macro F1 score = 0.13
(4) Percentage Classification accuracy = 23.33%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [6 0 0 0 0]
 [6 0 0 0 0]
 [6 0 0 0 0]
 [6 0 0 0 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]
 [0 0 6 0 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]
 [0 0 0 6 0]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[1 5 0 0 0]
 [0 6 0 0 0]
 [0 6 0 0 0]
 [0 6 0 0 0]
 [0 6 0 0 0]]
(2) micro F1 score = 0.23
(3) Macro F1 score = 0.13
(4) Percentage Classification accuracy = 23.33%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]
 [0 0 0 0 6]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.07
(4) Percentage Classification accuracy = 20.0%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[16 22 48 48 34]
 [12 24 48 48 36]
 [14 24 48 46 36]
 [12 24 48 48 36]
 [12 24 48 48 36]]
(2) Average micro F1 score = 0.2
(3) Average macro F1 score = 0.08
(4) Average Percentage Classification accuracy = 20.48%


LSTM_LOP0CV(hand_0, hand_1, model=LSTM_all_features_ratings(), num_folds=5, predicting_feature='rating_level_num', learning_rate=0.005, num_epochs=50, random_state=num) #, num_inner_folds=5

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[5 5 0]
 [7 8 0]
 [2 3 0]]
(2) micro F1 score = 0.43
(3) Weighted F1 score = 0.4
(4) Percentage Classification accuracy = 43.33%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[ 4  9  0]
 [ 0 17  0]
 [ 0  0  0]]
(2) micro F1 score = 0.7
(3) Weighted F1 score = 0.65
(4) Percentage Classification accuracy = 70.0%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[11  1  0]
 [ 2 11  0]
 [ 2  2  1]]
(2) micro F1 score = 0.77
(3) Weighted F1 score = 0.73
(4) Percentage Classification accuracy = 76.67%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[ 6  2  0]
 [ 0 19  0]
 [ 1  2  0]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.79
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[ 4  2  0]
 [ 0 15  0]
 [ 5  4  0]]
(2) micro F1 score = 0.63
(3) Weighted F1 score = 0.52
(4) Percentage Classification accuracy = 63.33%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[ 6  0  0]
 [ 0 15  0]
 [ 0  1  8]]
(2) micro F1 score = 0.97
(3) Weighted F1 score = 0.97
(4) Percentage Classification accuracy = 96.67%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[ 5  0  0]
 [ 0 16  0]
 [ 1  1  7]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.93
(4) Percentage Classification accuracy = 93.33%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[ 9  0  0]
 [ 0 16  0]
 [ 0  1  4]]
(2) micro F1 score = 0.97
(3) Weighted F1 score = 0.97
(4) Percentage Classification accuracy = 96.67%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[ 1  4  1]
 [ 0 16  0]
 [ 0  0  8]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.78
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[ 8  1  0]
 [ 0 17  0]
 [ 0  1  3]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.93
(4) Percentage Classification accuracy = 93.33%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[ 1  0  2]
 [ 1  2  2]
 [ 0  0 22]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.81
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[ 1  1  2]
 [ 0 14  0]
 [ 0  0 12]]
(2) micro F1 score = 0.9
(3) Weighted F1 score = 0.87
(4) Percentage Classification accuracy = 90.0%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[ 0  1  2]
 [ 0 19  0]
 [ 0  0  8]]
(2) micro F1 score = 0.9
(3) Weighted F1 score = 0.85
(4) Percentage Classification accuracy = 90.0%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[ 4  2  0]
 [ 0 17  0]
 [ 0  0  7]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.93
(4) Percentage Classification accuracy = 93.33%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[130  56  14]
 [ 20 404   4]
 [ 22  30 160]]
(2) Average micro F1 score = 0.83
(3) Average weighted F1 score = 0.8
(4) Average Percentage Classification accuracy = 82.62%


#hand0_emg = normalised_df.iloc[:,19:27]
hand1_emg = normalised_df.iloc[:,35:59]

emg_0 = normalised_df.iloc[:,:35]
emg_1 = pd.concat([df_info, hand1_emg], axis=1)


class LSTM_emg_features_properties(nn.Module):
    def __init__(self):
        super().__init__()
        self.rnn = nn.LSTM(24, 15, 1, batch_first=True)
        self.fc1 = nn.Linear(3 * 15 * 2, 25)
        self.fc2 = nn.Linear(25, 10)
        self.fc3 = nn.Linear(10, 5)
          
    def forward(self, x1, x2): #, x2
        x1, (hn, cn) = self.rnn(x1) #, (self.h0, self.c0)
        x1 = F.tanh(x1)
        x2, (hm, cm) = self.rnn(x2) # (self.h0, self.c0)
        x2 = F.tanh(x2)
        
        x = torch.cat((x1, x2), 2)
        
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x
    
class LSTM_emg_features_ratings(nn.Module):
    def __init__(self):
        super().__init__()
        self.rnn = nn.LSTM(24+5, 15, 1, batch_first=True)
        self.fc1 = nn.Linear(3 * 15 * 2, 25)
        self.fc2 = nn.Linear(25, 10)
        self.fc3 = nn.Linear(10, 3)
          
    def forward(self, x1, x2): #, x2
        x1, (hn, cn) = self.rnn(x1) #, (self.h0, self.c0)
        x1 = F.tanh(x1)
        x2, (hm, cm) = self.rnn(x2) # (self.h0, self.c0)
        x2 = F.tanh(x2)
        
        x = torch.cat((x1, x2), 2)
        
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x


LSTM_LOP0CV(emg_0, emg_1, model=LSTM_emg_features_properties(), num_folds=5, predicting_feature='property_id', learning_rate=0.005, num_epochs=70, random_state=num) #, num_inner_folds=5

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[2 0 4 0 0]
 [0 0 4 0 2]
 [5 0 1 0 0]
 [0 1 3 0 2]
 [1 0 2 0 3]]
(2) micro F1 score = 0.2
(3) Macro F1 score = 0.17
(4) Percentage Classification accuracy = 20.0%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[0 1 0 0 5]
 [0 3 0 0 3]
 [0 4 2 0 0]
 [0 2 0 3 1]
 [0 1 1 0 4]]
(2) micro F1 score = 0.4
(3) Macro F1 score = 0.38
(4) Percentage Classification accuracy = 40.0%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[5 0 1 0 0]
 [0 6 0 0 0]
 [0 0 6 0 0]
 [0 0 0 6 0]
 [0 0 0 0 6]]
(2) micro F1 score = 0.97
(3) Macro F1 score = 0.97
(4) Percentage Classification accuracy = 96.67%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[5 0 1 0 0]
 [0 5 0 1 0]
 [0 0 6 0 0]
 [0 0 0 6 0]
 [0 0 0 1 5]]
(2) micro F1 score = 0.9
(3) Macro F1 score = 0.9
(4) Percentage Classification accuracy = 90.0%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[4 1 0 0 1]
 [0 6 0 0 0]
 [0 0 5 0 1]
 [0 0 0 6 0]
 [0 0 0 0 6]]
(2) micro F1 score = 0.9
(3) Macro F1 score = 0.9
(4) Percentage Classification accuracy = 90.0%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[5 0 0 1 0]
 [0 5 0 0 1]
 [0 2 4 0 0]
 [0 0 0 6 0]
 [0 0 0 0 6]]
(2) micro F1 score = 0.87
(3) Macro F1 score = 0.86
(4) Percentage Classification accuracy = 86.67%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [0 5 1 0 0]
 [0 1 4 0 1]
 [0 0 0 6 0]
 [0 0 0 2 4]]
(2) micro F1 score = 0.83
(3) Macro F1 score = 0.83
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [0 6 0 0 0]
 [0 0 6 0 0]
 [0 2 0 3 1]
 [0 0 0 0 6]]
(2) micro F1 score = 0.9
(3) Macro F1 score = 0.89
(4) Percentage Classification accuracy = 90.0%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [0 6 0 0 0]
 [0 0 6 0 0]
 [0 0 0 6 0]
 [1 0 0 0 5]]
(2) micro F1 score = 0.97
(3) Macro F1 score = 0.97
(4) Percentage Classification accuracy = 96.67%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [0 6 0 0 0]
 [0 0 6 0 0]
 [0 0 0 6 0]
 [0 0 0 0 6]]
(2) micro F1 score = 1.0
(3) Macro F1 score = 1.0
(4) Percentage Classification accuracy = 100.0%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[5 1 0 0 0]
 [0 6 0 0 0]
 [1 0 5 0 0]
 [0 0 2 3 1]
 [1 0 0 1 4]]
(2) micro F1 score = 0.77
(3) Macro F1 score = 0.76
(4) Percentage Classification accuracy = 76.67%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [1 5 0 0 0]
 [1 0 3 2 0]
 [0 0 0 6 0]
 [0 2 0 2 2]]
(2) micro F1 score = 0.73
(3) Macro F1 score = 0.71
(4) Percentage Classification accuracy = 73.33%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[5 0 0 1 0]
 [0 5 0 0 1]
 [0 0 5 1 0]
 [0 0 0 6 0]
 [1 0 0 0 5]]
(2) micro F1 score = 0.87
(3) Macro F1 score = 0.87
(4) Percentage Classification accuracy = 86.67%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[6 0 0 0 0]
 [0 6 0 0 0]
 [0 0 6 0 0]
 [0 0 0 6 0]
 [0 2 1 0 3]]
(2) micro F1 score = 0.9
(3) Macro F1 score = 0.89
(4) Percentage Classification accuracy = 90.0%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[134   6  12   4  12]
 [  2 140  10   2  14]
 [ 14  14 130   6   4]
 [  0  10  10 138  10]
 [  8  10   8  12 130]]
(2) Average micro F1 score = 0.8
(3) Average macro F1 score = 0.79
(4) Average Percentage Classification accuracy = 80.0%


LSTM_LOP0CV(emg_0, emg_1, model=LSTM_emg_features_ratings(), num_folds=5, predicting_feature='rating_level_num', learning_rate=0.005, num_epochs=70, random_state=num) #, num_inner_folds=5

LEAVING PARTICIPANT 1 OUT:
The model for participant 1 has been saved
Leaving participant 1 out
(1) Confusion matrix:
 [[6 1 3]
 [7 1 7]
 [1 2 2]]
(2) micro F1 score = 0.3
(3) Weighted F1 score = 0.26
(4) Percentage Classification accuracy = 30.0%
--------------------------------
LEAVING PARTICIPANT 2 OUT:
The model for participant 2 has been saved
Leaving participant 2 out
(1) Confusion matrix:
 [[ 0 13  0]
 [ 0 17  0]
 [ 0  0  0]]
(2) micro F1 score = 0.57
(3) Weighted F1 score = 0.41
(4) Percentage Classification accuracy = 56.67%
--------------------------------
LEAVING PARTICIPANT 3 OUT:
The model for participant 3 has been saved
Leaving participant 3 out
(1) Confusion matrix:
 [[10  2  0]
 [ 0 13  0]
 [ 0  0  5]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.93
(4) Percentage Classification accuracy = 93.33%
--------------------------------
LEAVING PARTICIPANT 4 OUT:
The model for participant 4 has been saved
Leaving participant 4 out
(1) Confusion matrix:
 [[ 8  0  0]
 [ 0 19  0]
 [ 0  2  1]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.92
(4) Percentage Classification accuracy = 93.33%
--------------------------------
LEAVING PARTICIPANT 5 OUT:
The model for participant 5 has been saved
Leaving participant 5 out
(1) Confusion matrix:
 [[ 4  2  0]
 [ 0 15  0]
 [ 0  9  0]]
(2) micro F1 score = 0.63
(3) Weighted F1 score = 0.53
(4) Percentage Classification accuracy = 63.33%
--------------------------------
LEAVING PARTICIPANT 6 OUT:
The model for participant 6 has been saved
Leaving participant 6 out
(1) Confusion matrix:
 [[ 6  0  0]
 [ 1 14  0]
 [ 1  3  5]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.82
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 7 OUT:
The model for participant 7 has been saved
Leaving participant 7 out
(1) Confusion matrix:
 [[ 5  0  0]
 [ 0 14  2]
 [ 0  3  6]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.83
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 8 OUT:
The model for participant 8 has been saved
Leaving participant 8 out
(1) Confusion matrix:
 [[ 4  5  0]
 [ 0 16  0]
 [ 0  2  3]]
(2) micro F1 score = 0.77
(3) Weighted F1 score = 0.75
(4) Percentage Classification accuracy = 76.67%
--------------------------------
LEAVING PARTICIPANT 19 OUT:
The model for participant 19 has been saved
Leaving participant 19 out
(1) Confusion matrix:
 [[ 2  3  1]
 [ 0 16  0]
 [ 0  0  8]]
(2) micro F1 score = 0.87
(3) Weighted F1 score = 0.84
(4) Percentage Classification accuracy = 86.67%
--------------------------------
LEAVING PARTICIPANT 21 OUT:
The model for participant 21 has been saved
Leaving participant 21 out
(1) Confusion matrix:
 [[ 9  0  0]
 [ 0 17  0]
 [ 0  1  3]]
(2) micro F1 score = 0.97
(3) Weighted F1 score = 0.96
(4) Percentage Classification accuracy = 96.67%
--------------------------------
LEAVING PARTICIPANT 22 OUT:
The model for participant 22 has been saved
Leaving participant 22 out
(1) Confusion matrix:
 [[ 1  0  2]
 [ 0  2  3]
 [ 0  0 22]]
(2) micro F1 score = 0.83
(3) Weighted F1 score = 0.8
(4) Percentage Classification accuracy = 83.33%
--------------------------------
LEAVING PARTICIPANT 23 OUT:
The model for participant 23 has been saved
Leaving participant 23 out
(1) Confusion matrix:
 [[ 4  0  0]
 [ 0 12  2]
 [ 0  0 12]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.93
(4) Percentage Classification accuracy = 93.33%
--------------------------------
LEAVING PARTICIPANT 24 OUT:
The model for participant 24 has been saved
Leaving participant 24 out
(1) Confusion matrix:
 [[ 1  1  1]
 [ 0 19  0]
 [ 0  1  7]]
(2) micro F1 score = 0.9
(3) Weighted F1 score = 0.88
(4) Percentage Classification accuracy = 90.0%
--------------------------------
LEAVING PARTICIPANT 25 OUT:
The model for participant 25 has been saved
Leaving participant 25 out
(1) Confusion matrix:
 [[ 5  1  0]
 [ 0 17  0]
 [ 0  1  6]]
(2) micro F1 score = 0.93
(3) Weighted F1 score = 0.93
(4) Percentage Classification accuracy = 93.33%
--------------------------------
Using Leave One Participant Out CV (LOPOCV):
(1) Confusion matrix:
 [[130  56  14]
 [ 16 384  28]
 [  4  48 160]]
(2) Average micro F1 score = 0.8
(3) Average weighted F1 score = 0.77
(4) Average Percentage Classification accuracy = 80.24%


dtryftugyihuoi


def find_best_model_for_fold_4(train_dataloader, val_dataloader, learning_rate, num_epochs):

    # Model
    train_model = LSTM_2()

    # Loss and Optimiser
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = optim.SGD(train_model.parameters(), lr=learning_rate, momentum=0.7)

    best_avg_loss = np.inf
    best_model = None
    #best_model_epoch_num = np.inf
    train_loss_lst = []
    val_loss_lst = []
    avg_loss_lst = []

    for epoch in range(num_epochs):  # loop over the dataset multiple times

        #Set the model in training mode
        train_model.train()

        # Initialise the total training and validation loss
        epoch_train_loss = 0
        epoch_val_loss = 0
        avg_loss = 0
        #count_train = 0
        #count_val = 0

        #running_loss = 0.0
        for i, train_data in enumerate(train_dataloader, 0):
            #print(len(train_data))
            #count_train += 1
            # get the inputs; data is a list of [input1, input2, label]
            train_input1, train_input2, train_labels = train_data #train_input2, 

            #train_labels = train_labels.type(torch.LongTensor)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            train_preds = train_model(train_input1, train_input2)  
            #print(train_labels)#

            train_loss = criterion(train_preds, train_labels)
            train_loss.backward()
            optimizer.step()

            # Update training loss
            epoch_train_loss += train_loss.item()

        # Switch off auto grad for evaluation
        with torch.no_grad():

            # Set the model in evaluation mode
            train_model.eval()

            for j, val_data in enumerate(val_dataloader, 0):
               # count_val += 1
               # print(len(val_data))
            # get the inputs; data is a list of [input1, input2, label]
                val_input1, val_input2, val_labels = val_data # val_input2, 

                #val_labels = val_labels.type(torch.LongTensor)

                val_preds = train_model(val_input1, val_input2) #, val_input2

                # Update validation loss
                val_loss = criterion(val_preds, val_labels)

                epoch_val_loss += val_loss.item()
        #print(len(train_dataloader), len(val_dataloader))       
        avg_training_loss = epoch_train_loss / len(train_dataloader) #count_train
        avg_validation_loss = epoch_val_loss / len(val_dataloader) #count_val
        avg_loss = (avg_training_loss + avg_validation_loss) / 2
        
        train_loss_lst.append(avg_training_loss)
        val_loss_lst.append(avg_validation_loss)
        avg_loss_lst.append(avg_loss)
         
        #print(f'epoch {epoch+1}: train loss = {round(avg_training_loss,2)}, val loss = {round(avg_validation_loss,2)}, average loss = {round(avg_loss,2)}')

        if avg_loss < best_avg_loss:
            best_avg_loss = avg_loss
            best_model = train_model.state_dict()

    return best_avg_loss, best_model, train_loss_lst, val_loss_lst, avg_loss_lst   #, avg_loss_lst,


# Original
def LSTM_LOP0CV_properties_2(data_0, data_1, num_folds=5, predicting_feature='property_id', learning_rate=0.01, num_epochs=10, random_state=num): #, num_inner_folds=5
    # Set fixed random number seed
    torch.manual_seed(num)
    
   # elif predicting_feature == 'rating_level_num':
    #weighted_f1_lst = [] 
    total_conf_mat = 0
    micro_f1_lst = []
    macro_f1_lst = []
    acc_lst = []

    #lst = [6,24,2]#,24]
    
    for participant in sorted(data_0.participant_id.unique()): # #:lst: #
        print(f'LEAVING PARTICIPANT {participant} OUT:')
        
        # Split the data into training and testing
        training_data_0 = data_0[data_0.participant_id != participant] 
        training_data_1 = data_1[data_1.participant_id != participant] 
        testing_data_0 = data_0[data_0.participant_id == participant] 
        testing_data_1 = data_1[data_1.participant_id == participant] 

        # Data preparation
        X_train_0 = create_X_3d(training_data_0, 11)
        X_train_1 = create_X_3d(training_data_1, 11)        
        y_train = create_y_train_for_3d_X(training_data_0, predicting_feature = 'property_id')
        X_test_0 = create_X_3d(testing_data_0, 11) 
        X_test_1 = create_X_3d(testing_data_1, 11)
        y_test = create_y_test_for_3d_X(testing_data_0, predicting_feature = 'property_id')
        #print(y_test)

        #print(X_train_0.shape)
        #print(X_train_1.shape)
        #print(y_train.shape)
        #print(X_test_0.shape)
        #print(X_test_1.shape)
        #print(y_test.shape)
 
        # Create the datasets and dataloaders
        train_dataset = TensorDataset(X_train_0, X_train_1, y_train) 
        
        test_dataset = TensorDataset(X_test_0, X_test_1, y_test)
        test_dataloader = torch.utils.data.DataLoader(test_dataset, shuffle=True, batch_size=30) # num_workers=2,       
            
        # Configure the cross-validation procedure
        cv_inner = KFold(n_splits=num_folds, shuffle=True, random_state=num)
                
        min_avg_loss_subject = np.inf
        best_model_subject = None
        
        best_train_loss_lst = None
        best_val_loss_lst = None
        best_avg_loss_lst = None
        best_fold = np.inf

        for fold, (train_ids, val_ids) in enumerate(cv_inner.split(train_dataset)):
            #print(f'FOLD {fold+1}:')
                       
            # Sample elements randomly from a given list of ids, no replacement.
            train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids) # The ids are the same for both
            val_subsampler = torch.utils.data.SubsetRandomSampler(val_ids)

            # Define data loaders for training and testing data in this fold
            train_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=train_subsampler) #, batch_size=100
            val_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=val_subsampler) #, batch_size=20        

            avg_loss_fold, best_model_fold, fold_train_loss_lst, fold_val_loss_lst, fold_avg_loss_lst = find_best_model_for_fold_4(train_dataloader, val_dataloader, learning_rate, num_epochs)
                           
            if avg_loss_fold < min_avg_loss_subject:
                min_avg_loss_subject = avg_loss_fold
                best_model_subject = best_model_fold
                best_train_loss_lst = fold_train_loss_lst
                best_val_loss_lst = fold_val_loss_lst
                best_avg_loss_lst = fold_avg_loss_lst
                best_fold = fold+1

        fig = plt.figure(f"{participant}")                  
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_train_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_val_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_avg_loss_lst)
        plt.title(f"Loss when participant {participant} is left out for fold {best_fold} (lr={learning_rate}, num_epochs ={num_epochs})")
        plt.legend(['train loss', 'val loss', 'avg_loss'])

        # save trained model 
        name = 'model_participant_'+str(participant)+'.pt'
        torch.save(best_model_subject, name)
        print(f'The model for participant {participant} has been saved')
        
        test_model = LSTM_2()
        test_model.load_state_dict(torch.load(name))

        dataiter = iter(test_dataloader) 
        test_input1, test_input2, test_labels = dataiter.next() 
    
        test_preds = test_model(test_input1, test_input2) 
        
        test_preds_np = test_preds.detach().numpy()
        test_predicted_np = np.argmax(test_preds_np, axis = 1)
        
        test_labels_np = test_labels.numpy()       
        

        conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0, 1, 2, 3, 4])
        macro_f1_score = f1_score(test_labels_np, test_predicted_np, average='macro') 
        micro_f1_score = f1_score(test_labels_np, test_predicted_np, average='micro')  
        acc = accuracy_score(test_labels_np, test_predicted_np)

        print(f"Leaving participant {participant} out")
        print("(1) Confusion matrix:\n", conf_mat)
        print(f"(2) micro F1 score = {round(micro_f1_score,2)}")
        print(f"(3) macro F1 score = {round(macro_f1_score,2)}")    
        print(f"(4) Percentage Classification accuracy = {round(acc*100,2)}%")
        
        total_conf_mat += conf_mat 
        micro_f1_lst.append(micro_f1_score)
        macro_f1_lst.append(macro_f1_score) 
        acc_lst.append(acc)
        print('--------------------------------')
        
    avg_micro_f1_score = sum(micro_f1_lst) / len(micro_f1_lst)
    avg_macro_f1_score = sum(macro_f1_lst) / len(macro_f1_lst)         
    avg_acc = sum(acc_lst) / len(acc_lst)   
    
    print(f'Using Leave One Participant Out CV (LOPOCV):') 
    print("(1) Confusion matrix:\n", total_conf_mat)
    print(f"(2) Average micro F1 score = {round(avg_micro_f1_score,2)}")
    #if predicting_feature == 'property_id':
    print(f"(3) Average macro F1 score = {round(avg_macro_f1_score,2)}")
    #elif predicting_feature == 'rating_level_num':
        #print(f"(3) Average weighted F1 score = {round(avg_weighted_f1_score,2)}")      
    print(f"(4) Average Percentage Classification accuracy = {round(avg_acc*100,2)}%")


LSTM_LOP0CV_properties_2(emg_0, emg_1, num_folds=5, predicting_feature='property_id', learning_rate=0.05, num_epochs=70, random_state=num)


t2 = time.time()


complete_emg = normalised_df.iloc[:,:59]
hand0_emg = normalised_df.iloc[:,43:51]
hand0_emg.head()


hand0_emg = normalised_df.iloc[:,19:27]
hand1_emg = normalised_df.iloc[:,43:51]

emg_0 = pd.concat([df_info, hand0_emg], axis=1)
emg_1 = pd.concat([df_info, hand1_emg], axis=1)


class CNN_LSTM(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.conv1 = nn.Conv1d(in_channels=3, out_channels=3, kernel_size=1, stride=1, padding=0, groups=3) # Change kernel size to 3
        self.pool = nn.MaxPool1d(1, 1) # Change kernel size to 1 and stride to 1
        self.rnn = nn.LSTM(8, 4, 1, batch_first=True)
        #self.h0 = torch.randn(1, 50, 7)
        #self.c0 = torch.randn(1, 50, 7)
        self.fc1 = nn.Linear(3 * 4 * 2, 18)
        self.fc2 = nn.Linear(18, 10)
        self.fc3 = nn.Linear(10, 5)
          
    def forward(self, x1, x2): #, x2
        x1 = self.pool(F.tanh(self.conv1(x1))) # Use tanh instead?
        x2 = self.pool(F.tanh(self.conv1(x2))) # Use tanh instead?
        
        #x = self.pool(F.relu(self.conv2(x)))
        x1, (hn, cn) = self.rnn(x1) #, (self.h0, self.c0)
        x1 = F.tanh(x1)
        x2, (hm, cm) = self.rnn(x2) # (self.h0, self.c0)
        x2 = F.tanh(x2)
        
        x = torch.cat((x1, x2), 2)
        
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.tanh(self.fc1(x))
        x = F.tanh(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x


m3 = CNN_LSTM()
print(m3)


def find_best_model_for_fold_3(train_dataloader, val_dataloader, learning_rate, num_epochs):

    # Model
    train_model = CNN_LSTM()

    # Loss and Optimiser
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = optim.SGD(train_model.parameters(), lr=learning_rate, momentum=0.7)

    best_avg_loss = np.inf
    best_model = None
    #best_model_epoch_num = np.inf
    train_loss_lst = []
    val_loss_lst = []
    avg_loss_lst = []

    for epoch in range(num_epochs):  # loop over the dataset multiple times

        #Set the model in training mode
        train_model.train()

        # Initialise the total training and validation loss
        epoch_train_loss = 0
        epoch_val_loss = 0
        avg_loss = 0
        #count_train = 0
        #count_val = 0

        #running_loss = 0.0
        for i, train_data in enumerate(train_dataloader, 0):
            #print(len(train_data))
            #count_train += 1
            # get the inputs; data is a list of [input1, input2, label]
            train_input1, train_input2, train_labels = train_data #train_input2, 

            #train_labels = train_labels.type(torch.LongTensor)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            train_preds = train_model(train_input1, train_input2)  
            #print(train_labels)#

            train_loss = criterion(train_preds, train_labels)
            train_loss.backward()
            optimizer.step()

            # Update training loss
            epoch_train_loss += train_loss.item()

        # Switch off auto grad for evaluation
        with torch.no_grad():

            # Set the model in evaluation mode
            train_model.eval()

            for j, val_data in enumerate(val_dataloader, 0):
               # count_val += 1
               # print(len(val_data))
            # get the inputs; data is a list of [input1, input2, label]
                val_input1, val_input2, val_labels = val_data # val_input2, 

                #val_labels = val_labels.type(torch.LongTensor)

                val_preds = train_model(val_input1, val_input2) #, val_input2

                # Update validation loss
                val_loss = criterion(val_preds, val_labels)

                epoch_val_loss += val_loss.item()
        #print(len(train_dataloader), len(val_dataloader))       
        avg_training_loss = epoch_train_loss / len(train_dataloader) #count_train
        avg_validation_loss = epoch_val_loss / len(val_dataloader) #count_val
        avg_loss = (avg_training_loss + avg_validation_loss) / 2
        
        train_loss_lst.append(avg_training_loss)
        val_loss_lst.append(avg_validation_loss)
        avg_loss_lst.append(avg_loss)
         
        print(f'epoch {epoch+1}: train loss = {round(avg_training_loss,2)}, val loss = {round(avg_validation_loss,2)}, average loss = {round(avg_loss,2)}')

        if avg_loss < best_avg_loss:
            best_avg_loss = avg_loss
            best_model = train_model.state_dict()

    return best_avg_loss, best_model, train_loss_lst, val_loss_lst, avg_loss_lst   #, avg_loss_lst,


# Original
def CNN_LSTM_LOP0CV_properties_1(data_0, data_1, num_folds=5, predicting_feature='property_id', learning_rate=0.01, num_epochs=10, random_state=num): #, num_inner_folds=5
    # Set fixed random number seed
    torch.manual_seed(num)
    
   # elif predicting_feature == 'rating_level_num':
    #weighted_f1_lst = [] 
    total_conf_mat = 0
    micro_f1_lst = []
    macro_f1_lst = []
    acc_lst = []

    lst = [6,24,2]#,24]
    
    for participant in lst: #sorted(data0.participant_id.unique()): # #:lst: #
        print(f'LEAVING PARTICIPANT {participant} OUT:')
        
        # Split the data into training and testing
        training_data_0 = data_0[data_0.participant_id != participant] 
        training_data_1 = data_1[data_1.participant_id != participant] 
        testing_data_0 = data_0[data_0.participant_id == participant] 
        testing_data_1 = data_1[data_1.participant_id == participant] 

        # Data preparation
        X_train_0 = create_X_3d(training_data_0, 11)
        X_train_1 = create_X_3d(training_data_1, 11)        
        y_train = create_y_train_for_3d_X(training_data_0, predicting_feature = 'property_id')
        X_test_0 = create_X_3d(testing_data_0, 11) 
        X_test_1 = create_X_3d(testing_data_1, 11)
        y_test = create_y_test_for_3d_X(testing_data_0, predicting_feature = 'property_id')
        #print(y_test)

        #print(X_train_0.shape)
        #print(X_train_1.shape)
        #print(y_train.shape)
        #print(X_test_0.shape)
        #print(X_test_1.shape)
        #print(y_test.shape)
 
        # Create the datasets and dataloaders
        train_dataset = TensorDataset(X_train_0, X_train_1, y_train) 
        
        test_dataset = TensorDataset(X_test_0, X_test_1, y_test)
        test_dataloader = torch.utils.data.DataLoader(test_dataset, shuffle=True, batch_size=30) # num_workers=2,       
            
        # Configure the cross-validation procedure
        cv_inner = KFold(n_splits=num_folds, shuffle=True, random_state=num)
                
        min_avg_loss_subject = np.inf
        best_model_subject = None
        
        best_train_loss_lst = None
        best_val_loss_lst = None
        best_avg_loss_lst = None
        best_fold = np.inf

        for fold, (train_ids, val_ids) in enumerate(cv_inner.split(train_dataset)):
            #print(f'FOLD {fold+1}:')
                       
            # Sample elements randomly from a given list of ids, no replacement.
            train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids) # The ids are the same for both
            val_subsampler = torch.utils.data.SubsetRandomSampler(val_ids)

            # Define data loaders for training and testing data in this fold
            train_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=train_subsampler) #, batch_size=100
            val_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=val_subsampler) #, batch_size=20        

            avg_loss_fold, best_model_fold, fold_train_loss_lst, fold_val_loss_lst, fold_avg_loss_lst = find_best_model_for_fold_3(train_dataloader, val_dataloader, learning_rate, num_epochs)
                           
            if avg_loss_fold < min_avg_loss_subject:
                min_avg_loss_subject = avg_loss_fold
                best_model_subject = best_model_fold
                best_train_loss_lst = fold_train_loss_lst
                best_val_loss_lst = fold_val_loss_lst
                best_avg_loss_lst = fold_avg_loss_lst
                best_fold = fold+1

        fig = plt.figure(f"{participant}")                  
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_train_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_val_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_avg_loss_lst)
        plt.title(f"Loss when participant {participant} is left out for fold {best_fold} (lr={learning_rate}, num_epochs ={num_epochs})")
        plt.legend(['train loss', 'val loss', 'avg_loss'])

        # save trained model 
        name = 'model_participant_'+str(participant)+'.pt'
        torch.save(best_model_subject, name)
        print(f'The model for participant {participant} has been saved')
        
        test_model = CNN_LSTM()
        test_model.load_state_dict(torch.load(name))

        dataiter = iter(test_dataloader) 
        test_input1, test_input2, test_labels = dataiter.next() 
    
        test_preds = test_model(test_input1, test_input2) 
        
        test_preds_np = test_preds.detach().numpy()
        test_predicted_np = np.argmax(test_preds_np, axis = 1)
        
        test_labels_np = test_labels.numpy()       
        

        conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0, 1, 2, 3, 4])
        macro_f1_score = f1_score(test_labels_np, test_predicted_np, average='macro') 
        micro_f1_score = f1_score(test_labels_np, test_predicted_np, average='micro')  
        acc = accuracy_score(test_labels_np, test_predicted_np)

        print(f"Leaving participant {participant} out")
        print("(1) Confusion matrix:\n", conf_mat)
        print(f"(2) micro F1 score = {round(micro_f1_score,2)}")
        print(f"(3) macro F1 score = {round(macro_f1_score,2)}")    
        print(f"(4) Percentage Classification accuracy = {round(acc*100,2)}%")
        
        total_conf_mat += conf_mat 
        micro_f1_lst.append(micro_f1_score)
        macro_f1_lst.append(macro_f1_score) 
        acc_lst.append(acc)
        print('--------------------------------')
        
    avg_micro_f1_score = sum(micro_f1_lst) / len(micro_f1_lst)
    avg_macro_f1_score = sum(macro_f1_lst) / len(macro_f1_lst)         
    avg_acc = sum(acc_lst) / len(acc_lst)   
    
    print(f'Using Leave One Participant Out CV (LOPOCV):') 
    print("(1) Confusion matrix:\n", total_conf_mat)
    print(f"(2) Average micro F1 score = {round(avg_micro_f1_score,2)}")
    #if predicting_feature == 'property_id':
    print(f"(3) Average macro F1 score = {round(avg_macro_f1_score,2)}")
    #elif predicting_feature == 'rating_level_num':
        #print(f"(3) Average weighted F1 score = {round(avg_weighted_f1_score,2)}")      
    print(f"(4) Average Percentage Classification accuracy = {round(avg_acc*100,2)}%")


CNN_LSTM_LOP0CV_properties_1(emg_0, emg_1, num_folds=5, predicting_feature='property_id', learning_rate=0.3, num_epochs=30, random_state=num)


CNN_LSTM_LOP0CV_properties_1(emg_0, emg_1, num_folds=5, predicting_feature='property_id', learning_rate=0.3, num_epochs=30, random_state=num)


t2 = time.time()


def find_best_model_for_fold_2(train_dataloader, val_dataloader, learning_rate, num_epochs):

    # Model
    train_model = Linear_model_2()

    # Loss and Optimiser
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = optim.SGD(train_model.parameters(), lr=learning_rate, momentum=0.7)

    best_avg_loss = np.inf
    best_model = None
    #best_model_epoch_num = np.inf
    train_loss_lst = []
    val_loss_lst = []
    avg_loss_lst = []

    for epoch in range(num_epochs):  # loop over the dataset multiple times

        #Set the model in training mode
        train_model.train()

        # Initialise the total training and validation loss
        epoch_train_loss = 0
        epoch_val_loss = 0
        avg_loss = 0
        #count_train = 0
        #count_val = 0

        #running_loss = 0.0
        for i, train_data in enumerate(train_dataloader, 0):
            #print(len(train_data))
            #count_train += 1
            # get the inputs; data is a list of [input1, input2, label]
            train_input1, train_labels = train_data #train_input2, 

            #train_labels = train_labels.type(torch.LongTensor)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            train_preds = train_model(train_input1) #, train_input2
            #print(train_labels)#

            train_loss = criterion(train_preds, train_labels)
            train_loss.backward()
            optimizer.step()

            # Update training loss
            epoch_train_loss += train_loss.item()

        # Switch off auto grad for evaluation
        with torch.no_grad():

            # Set the model in evaluation mode
            train_model.eval()

            for j, val_data in enumerate(val_dataloader, 0):
               # count_val += 1
               # print(len(val_data))
            # get the inputs; data is a list of [input1, input2, label]
                val_input1, val_labels = val_data # val_input2, 

                #val_labels = val_labels.type(torch.LongTensor)

                val_preds = train_model(val_input1) #, val_input2

                # Update validation loss
                val_loss = criterion(val_preds, val_labels)

                epoch_val_loss += val_loss.item()
        #print(len(train_dataloader), len(val_dataloader))       
        avg_training_loss = epoch_train_loss / len(train_dataloader) #count_train
        avg_validation_loss = epoch_val_loss / len(val_dataloader) #count_val
        avg_loss = (avg_training_loss + avg_validation_loss) / 2
        
        train_loss_lst.append(avg_training_loss)
        val_loss_lst.append(avg_validation_loss)
        avg_loss_lst.append(avg_loss)
         
        #print(f'epoch {epoch+1}: train loss = {round(avg_training_loss,2)}, val loss = {round(avg_validation_loss,2)}, average loss = {round(avg_loss,2)}')

        if avg_loss < best_avg_loss:
            best_avg_loss = avg_loss
            best_model = train_model.state_dict()

    return best_avg_loss, best_model, train_loss_lst, val_loss_lst, avg_loss_lst   #, avg_loss_lst,


# Original
def Linear_LOP0CV_properties_2(data, num_folds=5, predicting_feature='property_id', learning_rate=0.01, num_epochs=10, random_state=num, X_dim=2): #, num_inner_folds=5
    # Set fixed random number seed
    torch.manual_seed(num)
    
   # elif predicting_feature == 'rating_level_num':
    #weighted_f1_lst = [] 
    total_conf_mat = 0
    micro_f1_lst = []
    macro_f1_lst = []
    acc_lst = []

    #lst = [6,24,2]#,24]
    
    for participant in sorted(data.participant_id.unique()): # #:lst: #
        print(f'LEAVING PARTICIPANT {participant} OUT:')
        
        # Split the data into training and testing
        training_data = data[data.participant_id != participant] 
        testing_data = data[data.participant_id == participant] 

        # Data preparation
        if X_dim == 2:
            X_train = create_X_2d(training_data, 11)       
            y_train = create_y_train_for_2d_X(training_data, predicting_feature = 'property_id')
            X_test = create_X_2d(testing_data, 11) 
            y_test = create_y_test_for_2d_X(testing_data, predicting_feature = 'property_id')
        elif X_dim == 3:
            X_train = create_X_3d(training_data, 11)       
            y_train = create_y_train_for_3d_X(training_data, predicting_feature = 'property_id')
            X_test = create_X_3d(testing_data, 11) 
            y_test = create_y_test_for_3d_X(testing_data, predicting_feature = 'property_id')

        #print(X_train.shape)
        #print(y_train.shape)
        #print(X_test.shape)
        #print(y_test.shape)
 
        # Create the datasets and dataloaders
        train_dataset = TensorDataset(X_train, y_train) 
        
        test_dataset = TensorDataset(X_test, y_test)
        test_dataloader = torch.utils.data.DataLoader(test_dataset, shuffle=True, batch_size=90) # num_workers=2,       
            
        # Configure the cross-validation procedure
        cv_inner = KFold(n_splits=num_folds, shuffle=True, random_state=num)
                
        min_avg_loss_subject = np.inf
        best_model_subject = None
        
        best_train_loss_lst = None
        best_val_loss_lst = None
        best_avg_loss_lst = None
        best_fold = np.inf

        for fold, (train_ids, val_ids) in enumerate(cv_inner.split(train_dataset)):
            #print(f'FOLD {fold+1}:')
                       
            # Sample elements randomly from a given list of ids, no replacement.
            train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids) # The ids are the same for both
            val_subsampler = torch.utils.data.SubsetRandomSampler(val_ids)

            # Define data loaders for training and testing data in this fold
            train_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=train_subsampler) #, batch_size=100
            val_dataloader = torch.utils.data.DataLoader(train_dataset, sampler=val_subsampler) #, batch_size=20        

            avg_loss_fold, best_model_fold, fold_train_loss_lst, fold_val_loss_lst, fold_avg_loss_lst = find_best_model_for_fold_2(train_dataloader, val_dataloader, learning_rate, num_epochs)
                           
            if avg_loss_fold < min_avg_loss_subject:
                min_avg_loss_subject = avg_loss_fold
                best_model_subject = best_model_fold
                best_train_loss_lst = fold_train_loss_lst
                best_val_loss_lst = fold_val_loss_lst
                best_avg_loss_lst = fold_avg_loss_lst
                best_fold = fold+1

        fig = plt.figure(f"{participant}")                  
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_train_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_val_loss_lst)
        plt.plot(np.linspace(1, num_epochs, num_epochs).astype(int), best_avg_loss_lst)
        plt.title(f"Loss when participant {participant} is left out for fold {best_fold} (lr={learning_rate}, num_epochs ={num_epochs})")
        plt.legend(['train loss', 'val loss', 'avg_loss'])

        # save trained model 
        name = 'model_participant_'+str(participant)+'.pt'
        torch.save(best_model_subject, name)
        print(f'The model for participant {participant} has been saved')
        
        test_model = Linear_model_2()
        test_model.load_state_dict(torch.load(name))

        dataiter = iter(test_dataloader) 
        test_input1, test_labels = dataiter.next() 
    
        test_preds = test_model(test_input1) 
        
        test_preds_np = test_preds.detach().numpy()
        test_predicted_np = np.argmax(test_preds_np, axis = 1)
        
        test_labels_np = test_labels.numpy()       
        

        conf_mat = confusion_matrix(test_labels_np, test_predicted_np, labels=[0, 1, 2, 3, 4])
        macro_f1_score = f1_score(test_labels_np, test_predicted_np, average='macro') 
        micro_f1_score = f1_score(test_labels_np, test_predicted_np, average='micro')  
        acc = accuracy_score(test_labels_np, test_predicted_np)

        print(f"Leaving participant {participant} out")
        print("(1) Confusion matrix:\n", conf_mat)
        print(f"(2) micro F1 score = {round(micro_f1_score,2)}")
        print(f"(3) macro F1 score = {round(macro_f1_score,2)}")    
        print(f"(4) Percentage Classification accuracy = {round(acc*100,2)}%")
        
        total_conf_mat += conf_mat 
        micro_f1_lst.append(micro_f1_score)
        macro_f1_lst.append(macro_f1_score) 
        acc_lst.append(acc)
        print('--------------------------------')
        
    avg_micro_f1_score = sum(micro_f1_lst) / len(micro_f1_lst)
    avg_macro_f1_score = sum(macro_f1_lst) / len(macro_f1_lst)         
    avg_acc = sum(acc_lst) / len(acc_lst)   
    
    print(f'Using Leave One Participant Out CV (LOPOCV):') 
    print("(1) Confusion matrix:\n", total_conf_mat)
    print(f"(2) Average micro F1 score = {round(avg_micro_f1_score,2)}")
    #if predicting_feature == 'property_id':
    print(f"(3) Average macro F1 score = {round(avg_macro_f1_score,2)}")
    #elif predicting_feature == 'rating_level_num':
        #print(f"(3) Average weighted F1 score = {round(avg_weighted_f1_score,2)}")      
    print(f"(4) Average Percentage Classification accuracy = {round(avg_acc*100,2)}%")

	participant_id	clothes_id	property_id	property_name	interaction_id	rating	rating_level	rating_level_num	sub_window_num	subwindow_start_time	subwindow_end_time	max_ch1_Hand0	max_ch2_Hand0	max_ch3_Hand0	max_ch4_Hand0	max_ch5_Hand0	max_ch6_Hand0	max_ch7_Hand0	max_ch8_Hand0	mean_ch1_Hand0	mean_ch2_Hand0	mean_ch3_Hand0	mean_ch4_Hand0	mean_ch5_Hand0	mean_ch6_Hand0	mean_ch7_Hand0	mean_ch8_Hand0	std_ch1_Hand0	std_ch2_Hand0	std_ch3_Hand0	std_ch4_Hand0	std_ch5_Hand0	std_ch6_Hand0	std_ch7_Hand0	std_ch8_Hand0	max_ch1_Hand1	max_ch2_Hand1	max_ch3_Hand1	max_ch4_Hand1	max_ch5_Hand1	max_ch6_Hand1	max_ch7_Hand1	max_ch8_Hand1	mean_ch1_Hand1	mean_ch2_Hand1	mean_ch3_Hand1	mean_ch4_Hand1	mean_ch5_Hand1	mean_ch6_Hand1	mean_ch7_Hand1	mean_ch8_Hand1	std_ch1_Hand1	std_ch2_Hand1	std_ch3_Hand1	std_ch4_Hand1	std_ch5_Hand1	std_ch6_Hand1	std_ch7_Hand1	std_ch8_Hand1	max_Ax_Hand0	max_Ay_Hand0	max_Az_Hand0	max_Vx_Hand0	max_Vy_Hand0	max_Vz_Hand0	max_Jx_Hand0	max_Jy_Hand0	max_Jz_Hand0	mean_Ax_Hand0	mean_Ay_Hand0	mean_Az_Hand0	mean_Vx_Hand0	mean_Vy_Hand0	mean_Vz_Hand0	mean_Jx_Hand0	mean_Jy_Hand0	mean_Jz_Hand0	std_Ax_Hand0	std_Ay_Hand0	std_Az_Hand0	std_Vx_Hand0	std_Vy_Hand0	std_Vz_Hand0	std_Jx_Hand0	std_Jy_Hand0	std_Jz_Hand0	max_Ax_Hand1	max_Ay_Hand1	max_Az_Hand1	max_Vx_Hand1	max_Vy_Hand1	max_Vz_Hand1	max_Jx_Hand1	max_Jy_Hand1	max_Jz_Hand1	mean_Ax_Hand1	mean_Ay_Hand1	mean_Az_Hand1	mean_Vx_Hand1	mean_Vy_Hand1	mean_Vz_Hand1	mean_Jx_Hand1	mean_Jy_Hand1	mean_Jz_Hand1	std_Ax_Hand1	std_Ay_Hand1	std_Az_Hand1	std_Vx_Hand1	std_Vy_Hand1	std_Vz_Hand1	std_Jx_Hand1	std_Jy_Hand1	std_Jz_Hand1	max_w_Hand0	max_x_Hand0	max_y_Hand0	max_z_Hand0	max_AVx_Hand0	max_AVy_Hand0	max_AVz_Hand0	max_AAx_Hand0	max_AAy_Hand0	max_AAz_Hand0	max_AJx_Hand0	max_AJy_Hand0	max_AJz_Hand0	mean_w_Hand0	mean_x_Hand0	mean_y_Hand0	mean_z_Hand0	mean_AVx_Hand0	mean_AVy_Hand0	mean_AVz_Hand0	mean_AAx_Hand0	mean_AAy_Hand0	mean_AAz_Hand0	mean_AJx_Hand0	mean_AJy_Hand0	mean_AJz_Hand0	std_w_Hand0	std_x_Hand0	std_y_Hand0	std_z_Hand0	std_AVx_Hand0	std_AVy_Hand0	std_AVz_Hand0	std_AAx_Hand0	std_AAy_Hand0	std_AAz_Hand0	std_AJx_Hand0	std_AJy_Hand0	std_AJz_Hand0	max_w_Hand1	max_x_Hand1	max_y_Hand1	max_z_Hand1	max_AVx_Hand1	max_AVy_Hand1	max_AVz_Hand1	max_AAx_Hand1	max_AAy_Hand1	max_AAz_Hand1	max_AJx_Hand1	max_AJy_Hand1	max_AJz_Hand1	mean_w_Hand1	mean_x_Hand1	mean_y_Hand1	mean_z_Hand1	mean_AVx_Hand1	mean_AVy_Hand1	mean_AVz_Hand1	mean_AAx_Hand1	mean_AAy_Hand1	mean_AAz_Hand1	mean_AJx_Hand1	mean_AJy_Hand1	mean_AJz_Hand1	std_w_Hand1	std_x_Hand1	std_y_Hand1	std_z_Hand1	std_AVx_Hand1	std_AVy_Hand1	std_AVz_Hand1	std_AAx_Hand1	std_AAy_Hand1	std_AAz_Hand1	std_AJx_Hand1	std_AJy_Hand1	std_AJz_Hand1
0	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.504132	0.620513	0.675978	0.500000	0.759036	0.652406	0.76250	0.666667	0.497498	0.612780	0.665895	0.492285	0.718526	0.636907	0.748853	0.660720	0.002329	0.002760	0.003011	0.002201	0.006194	0.004289	0.003634	0.002595	0.508197	0.929578	0.757225	0.730159	0.497959	0.995918	0.663366	0.580645	0.497102	0.848495	0.692072	0.634859	0.491574	0.511567	0.596728	0.558018	0.003056	0.009851	0.011220	0.016046	0.002283	0.125342	0.014496	0.005100	-0.850708	-0.068771	0.490311	0.000525	0.000406	0.000903	1.269531	1.220703	2.258301	-0.863979	-0.089162	0.468123	-4.589844e-06	1.483398e-05	-1.191406e-05	-0.010928	-0.052313	0.060087	0.006994	0.008424	0.010178	0.000201	0.000180	0.000270	0.599364	0.630856	0.794158	1.023270	0.042786	-0.178879	0.001218	0.001529	0.001429	4.089355	5.468750	3.271484	0.970242	-0.011443	-0.239064	-3.020508e-05	-8.115234e-06	0.000065	0.061211	0.036287	-0.170119	0.017187	0.014988	0.034359	0.000440	0.000417	0.000641	1.262790	1.374306	1.977670	0.179352	0.488418	0.160615	-0.840857	0.133141	0.061469	0.117029	4.332320	2.887801	19.812834	554.574503	316.450316	1338.775861	0.171550	0.486515	0.155002	-0.842509	-0.002358	-0.006487	-0.037808	-0.069210	0.040128	0.213198	-18.896953	-5.997847	29.264918	0.004033	0.001122	0.003172	0.000812	0.034035	0.027288	0.100030	1.808784	1.349269	6.515867	214.502864	135.316892	572.754308	0.618680	-0.175326	-0.753378	-0.128901	0.215491	0.583907	0.275227	21.603607	56.450019	29.424453	2797.763022	9050.317251	2366.953044	0.602964	-0.177295	-0.766545	-0.131596	-0.011132	-0.035192	-0.000290	-0.692534	-0.994509	-0.440578	19.388504	94.341574	-56.539654	0.007223	0.001357	0.006089	0.001773	0.140168	0.419609	0.109731	8.409602	20.970763	7.985693	769.853603	1878.284384	898.072858
1	7	14	3	smoothness	1	3	medium	2	2	2021-08-05 13:21:32.495	2021-08-05 13:21:33.464	0.504132	0.620513	0.675978	0.500000	0.759036	0.663102	0.75625	0.666667	0.497417	0.612789	0.666077	0.492186	0.718908	0.637194	0.748828	0.660428	0.002321	0.002785	0.002810	0.002245	0.006368	0.004426	0.002994	0.002613	0.508197	0.957746	0.809249	0.746032	0.497959	0.995918	0.683168	0.585254	0.496918	0.848890	0.692686	0.634612	0.491212	0.492734	0.596994	0.558059	0.003603	0.012522	0.017839	0.015241	0.002220	0.168270	0.014712	0.007247	-0.846313	-0.069260	0.488846	0.000545	0.000305	0.000813	2.929688	1.190186	3.580729	-0.863563	-0.084855	0.464930	-6.825973e-06	2.042809e-06	-2.849968e-06	0.048335	-0.042143	0.040023	0.006822	0.005053	0.009881	0.000240	0.000117	0.000297	0.712536	0.499915	0.939672	0.999344	0.062317	-0.159836	0.001187	0.001328	0.001074	3.255208	3.320312	4.882812	0.965975	-0.000925	-0.259182	3.190430e-05	6.406250e-05	-0.000024	-0.095776	-0.165980	0.144850	0.015653	0.019206	0.046904	0.000333	0.000457	0.000460	1.187168	1.426576	1.346959	0.164387	0.492346	0.149139	-0.843806	0.051026	0.074743	0.071737	8.423988	6.857490	10.689540	2084.369900	445.379626	810.506826	0.160231	0.490322	0.145928	-0.844157	-0.003892	-0.003804	-0.018201	-0.036467	0.077885	-0.006631	26.988689	-7.739067	-51.168052	0.002767	0.001331	0.002246	0.000222	0.031651	0.021172	0.067094	1.891410	1.482885	4.074346	324.990077	135.612823	500.564267	0.618863	-0.150822	-0.754349	-0.114060	0.383834	0.784153	0.553013	11.380460	29.010612	33.358820	2752.365093	6546.331906	2181.850987	0.596188	-0.163353	-0.775710	-0.124714	0.021907	0.013755	0.012748	-1.111580	-0.334962	0.021466	-90.668628	-64.601575	35.073660	0.015482	0.009833	0.014861	0.007212	0.166403	0.417988	0.173217	7.359519	14.858287	9.153837	744.182656	1454.186126	769.701026
2	7	14	3	smoothness	1	3	medium	2	3	2021-08-05 13:21:33.513	2021-08-05 13:21:46.475	0.685950	0.728205	0.849162	0.607438	0.777108	0.700535	0.87500	0.733333	0.497469	0.612832	0.666206	0.492255	0.718535	0.637126	0.748788	0.660685	0.011144	0.006879	0.009090	0.007845	0.005614	0.004051	0.005506	0.004440	0.516393	0.936620	0.832370	0.751323	0.995918	0.995918	0.698020	0.622120	0.496948	0.848779	0.691786	0.634099	0.486646	0.498963	0.595900	0.557786	0.003032	0.007777	0.009667	0.009077	0.043924	0.108942	0.010389	0.006009	-0.713501	0.048904	0.572830	0.006123	0.002393	0.002616	15.869141	6.692325	6.749770	-0.868275	-0.069352	0.459729	-2.960650e-05	-6.790516e-07	-3.816164e-06	0.042151	0.006334	-0.003044	0.024366	0.017504	0.021913	0.000905	0.000376	0.000411	1.608533	0.921114	1.017115	1.060379	0.134583	-0.014328	0.001955	0.002280	0.005502	11.568510	13.627486	7.359096	0.978988	-0.000388	-0.202378	4.738978e-06	2.112100e-07	-0.000013	-0.005919	-0.001242	0.023884	0.015082	0.015613	0.041390	0.000334	0.000404	0.000483	1.085616	1.194701	1.336285	0.168521	0.505227	0.142912	-0.835808	0.316624	0.593621	0.331014	19.806659	22.033338	26.051293	2206.180328	2613.281789	2713.864594	0.155634	0.496838	0.135235	-0.842923	-0.001639	-0.004225	0.000291	-0.003520	-0.114047	0.050148	1.585695	-0.670795	3.401131	0.005331	0.007307	0.004144	0.004806	0.054181	0.088889	0.070436	2.939673	3.487398	4.355037	268.202026	277.300592	448.167800	0.647831	-0.136257	-0.732800	-0.102524	0.879971	1.989502	1.122907	49.389847	85.475258	61.100255	4256.775521	8723.328584	4294.956003	0.619735	-0.153120	-0.759202	-0.124798	0.001542	0.004674	-0.003122	0.079193	0.174868	0.141086	4.852312	18.350814	2.436863	0.012631	0.008083	0.012225	0.012151	0.123687	0.282669	0.113030	4.806996	10.779876	6.534156	486.415887	1142.095742	625.153662
3	7	14	4	thickness	2	5	medium	2	1	2021-08-05 13:22:02.060	2021-08-05 13:22:03.029	0.512397	0.661538	0.754190	0.512397	0.759036	0.673797	0.76250	0.677778	0.497326	0.612893	0.666100	0.484996	0.718373	0.637000	0.748664	0.660854	0.004125	0.014409	0.021173	0.058099	0.007076	0.005177	0.005281	0.004803	0.516393	0.873239	0.716763	0.666667	0.493878	0.497959	0.643564	0.580645	0.496862	0.848702	0.691813	0.634084	0.491064	0.493647	0.593827	0.557622	0.003913	0.006136	0.005968	0.004635	0.002087	0.001368	0.008473	0.005300	-0.724731	0.207108	0.604568	0.001240	0.002000	0.000923	6.787109	3.352865	2.838135	-0.789386	0.166772	0.569221	-1.656327e-05	5.648744e-06	6.577436e-06	0.128443	-0.119382	0.047230	0.021868	0.015788	0.017773	0.000546	0.000486	0.000461	1.863614	1.430691	1.313220	1.033524	0.126770	0.421707	0.001523	0.000680	0.001583	5.789621	3.756010	5.208333	0.939998	0.078811	0.345779	-5.450195e-05	-9.718750e-05	-0.000015	0.111309	0.174966	-0.011341	0.032360	0.014418	0.032139	0.000701	0.000572	0.000436	1.909429	1.065716	1.424733	0.211566	0.465286	0.013511	-0.859402	0.093546	0.263660	0.136374	10.712655	24.969117	24.077240	2666.816885	1920.723426	5029.040161	0.205977	0.460094	0.012212	-0.863552	0.000240	0.012612	-0.032937	0.277966	0.539358	-0.114937	39.024051	-14.919398	87.718777	0.002371	0.002726	0.000789	0.001891	0.062111	0.086016	0.096223	3.429911	6.023802	6.526026	441.445219	477.288587	875.383706	0.733975	-0.215858	-0.515914	-0.370804	0.271520	0.413471	0.793342	29.883033	33.672845	86.168465	3965.979471	4675.769180	11733.432855	0.726422	-0.222149	-0.527491	-0.380252	-0.081652	-0.042828	0.027246	-1.330705	-1.340691	1.024436	-49.463745	-93.159203	-58.539333	0.005213	0.004116	0.008078	0.003958	0.198462	0.183171	0.155875	11.152448	9.963270	16.088213	1150.663729	1135.642103	2119.655762
4	7	14	4	thickness	2	5	medium	2	2	2021-08-05 13:22:03.061	2021-08-05 13:22:04.047	0.504132	0.635897	0.737430	1.000000	0.746988	0.652406	0.76250	0.672222	0.497305	0.612760	0.666103	0.539813	0.720609	0.636990	0.748779	0.661079	0.003440	0.007323	0.011248	0.095872	0.007634	0.003822	0.004761	0.004264	0.512295	0.866197	0.710983	0.650794	0.497959	0.497959	0.722772	0.599078	0.496943	0.848932	0.691707	0.633865	0.491129	0.493722	0.594159	0.558040	0.003295	0.004746	0.003927	0.004114	0.002148	0.001221	0.017247	0.007301	-0.774048	0.188553	0.582108	0.000586	0.000533	0.000714	2.859933	1.497396	2.392578	-0.796210	0.169789	0.556567	-1.621293e-05	1.522640e-05	-6.975446e-08	0.037976	-0.004908	0.032503	0.010191	0.008518	0.010531	0.000297	0.000196	0.000290	0.819147	0.545627	0.845935	1.021317	0.099915	0.340652	0.002075	0.001179	0.000612	3.200955	1.819957	1.864347	0.956346	0.070920	0.300213	4.262695e-05	9.736328e-06	-0.000034	-0.036841	-0.036219	0.075440	0.021201	0.012754	0.029698	0.000486	0.000285	0.000374	1.348408	0.832293	0.830523	0.202624	0.467448	0.012214	-0.860776	0.052285	0.040986	0.116178	2.856049	3.770583	15.126930	312.982165	281.025841	1062.566337	0.200699	0.466227	0.007997	-0.861557	-0.006016	-0.012182	-0.007998	0.021963	0.064504	0.191972	7.439429	3.549816	8.698628	0.001243	0.001357	0.001932	0.000563	0.026385	0.029268	0.078408	1.107083	1.317382	4.622568	85.712120	106.001201	406.024850	0.722931	-0.229336	-0.534479	-0.366098	0.563110	0.565189	0.085749	12.485845	20.251992	7.395605	1233.738922	2111.723609	964.527774	0.715442	-0.232278	-0.544587	-0.370770	0.045293	0.053730	-0.001529	-0.013939	0.110153	-0.246766	-31.768089	-36.879092	-7.538979	0.006682	0.002770	0.009360	0.002573	0.181719	0.217952	0.053026	5.253641	7.297097	3.331552	548.393076	819.998059	375.285448
5	7	14	4	thickness	2	5	medium	2	3	2021-08-05 13:22:04.089	2021-08-05 13:22:17.056	0.553719	0.702564	0.798883	1.000000	0.843374	0.754011	0.85625	0.838889	0.497505	0.613086	0.666296	0.504482	0.719296	0.637041	0.748460	0.661134	0.005483	0.014608	0.021523	0.086024	0.013988	0.011014	0.010647	0.012910	0.516393	0.908451	0.757225	1.000000	0.506122	0.995918	0.737624	0.631336	0.496977	0.848712	0.691639	0.634160	0.491135	0.498371	0.594707	0.557902	0.003472	0.006247	0.005734	0.009682	0.002139	0.072799	0.016020	0.008990	-0.495728	0.442459	0.703690	0.005518	0.006189	0.002979	15.733507	11.596680	9.277344	-0.758163	0.214571	0.594610	9.725811e-06	-4.071514e-07	-8.804087e-07	-0.007886	-0.011941	0.011571	0.040677	0.046681	0.028793	0.000964	0.000991	0.000583	2.786852	2.859017	1.729196	1.025223	0.165344	0.481277	0.005273	0.002097	0.005072	11.779785	7.742746	4.296875	0.930939	0.045716	0.377158	1.475377e-06	7.309924e-06	0.000006	-0.022335	-0.028634	-0.001207	0.024952	0.021866	0.034461	0.000633	0.000371	0.000427	1.557721	1.131095	1.057328	0.304236	0.469451	0.041448	-0.850884	0.835660	0.523349	0.701497	47.757157	29.508942	99.417330	3720.608701	3339.189714	12240.574053	0.201429	0.444563	-0.022133	-0.871543	-0.006513	0.000234	-0.012988	-0.050343	-0.140312	0.216882	0.530592	-3.321829	32.290963	0.032100	0.010512	0.022419	0.008509	0.135674	0.107296	0.181583	6.942452	5.329504	9.024727	633.454890	467.112285	938.088172	0.755172	-0.198675	-0.489715	-0.348140	0.645748	0.659454	0.690995	33.174078	30.444952	23.711127	4015.796847	2850.002331	3281.919685	0.740970	-0.226013	-0.510855	-0.372205	0.003137	0.006422	0.000006	0.087152	0.043305	0.032090	-4.054701	-1.942459	2.477150	0.008967	0.007630	0.014624	0.004419	0.136329	0.136392	0.080490	5.948223	5.273546	4.484067	548.302677	461.158083	430.242493
6	7	14	5	warmth	3	4	medium	2	1	2021-08-05 13:22:31.916	2021-08-05 13:22:32.885	0.541322	0.635897	0.737430	1.000000	0.969880	0.754011	0.90000	0.711111	0.497367	0.613213	0.666133	0.531258	0.720450	0.637356	0.748576	0.661156	0.008533	0.007315	0.014736	0.137396	0.020352	0.014732	0.025558	0.008438	0.504098	0.859155	0.699422	0.645503	0.493878	0.591837	0.633663	0.571429	0.496926	0.848481	0.691474	0.634001	0.491215	0.494173	0.594639	0.557757	0.002602	0.004765	0.003895	0.004512	0.002198	0.022021	0.009568	0.003340	-0.630493	0.292557	0.725662	0.001993	0.001768	0.002492	14.439174	4.418945	6.011963	-0.754536	0.110555	0.615984	-1.627930e-05	-1.258984e-04	4.658203e-05	0.367935	-0.548133	0.157648	0.053432	0.106904	0.055757	0.000789	0.000949	0.000699	2.943439	3.500887	1.754305	0.990555	-0.065125	0.314285	0.000483	0.000718	0.001211	4.123264	3.662109	1.464844	0.959209	-0.098711	0.290963	1.596009e-05	-2.948836e-05	0.000020	0.070368	0.034191	-0.107028	0.012857	0.009945	0.013171	0.000245	0.000285	0.000335	0.961570	0.911666	0.941243	0.200454	0.461937	0.110280	-0.866305	1.129284	1.729592	3.032375	55.003635	56.696369	203.242726	4136.161069	4946.896019	15228.395256	0.189911	0.430333	0.024649	-0.880448	0.229305	0.227613	0.147295	0.223691	0.297201	0.580471	-87.960280	-106.613553	-240.938226	0.004193	0.029433	0.044366	0.012622	0.292127	0.402421	0.602283	13.551026	16.513205	40.830863	1344.718745	1649.489996	3942.378448	0.806178	-0.075378	-0.584129	-0.022996	0.074675	0.221001	0.131363	9.875660	10.772344	33.755006	1151.414467	923.423614	4828.801803	0.803182	-0.083425	-0.589053	-0.030118	-0.011975	0.014500	-0.057538	0.409021	-0.526483	0.035310	20.598778	-33.044542	53.587612	0.002457	0.004266	0.003681	0.003448	0.039194	0.114553	0.133050	2.240763	4.172808	10.907450	282.637410	364.125511	1378.104484
7	7	14	5	warmth	3	4	medium	2	2	2021-08-05 13:22:32.921	2021-08-05 13:22:33.908	0.516529	0.630769	0.715084	1.000000	0.771084	0.743316	1.00000	0.716667	0.497377	0.612720	0.666026	0.534026	0.720280	0.637084	0.749341	0.660992	0.004428	0.004363	0.008239	0.138922	0.010592	0.015469	0.032984	0.008952	0.504098	0.859155	0.699422	0.645503	0.493878	0.669388	0.648515	0.571429	0.496918	0.848688	0.691666	0.633888	0.491040	0.494061	0.594707	0.557829	0.002429	0.004383	0.003645	0.003689	0.001949	0.018184	0.007479	0.002981	-0.637817	0.085037	0.705154	0.002688	0.002539	0.001668	11.946615	10.742188	7.136418	-0.769683	-0.069289	0.609881	-1.024707e-04	-3.987305e-05	-1.062695e-04	0.130856	0.195704	0.049857	0.056362	0.051264	0.048677	0.001089	0.001192	0.000845	2.922376	3.486885	1.934238	0.982742	-0.081726	0.315750	0.000574	0.000500	0.000831	1.953125	0.773112	1.403809	0.957391	-0.094119	0.293777	-5.244141e-06	1.746094e-05	0.000016	-0.027315	-0.020972	-0.085668	0.010017	0.007187	0.013378	0.000202	0.000157	0.000260	0.600097	0.424049	0.679792	0.193081	0.444410	0.110763	-0.883228	0.953516	0.497843	0.562688	28.047074	51.438817	114.042184	2597.360965	2336.045220	7348.000102	0.124725	0.426547	0.096821	-0.889832	0.069070	-0.070847	-0.221369	-0.594438	0.253096	-1.259279	3.884025	6.008674	-192.191792	0.032551	0.015930	0.005492	0.003001	0.216621	0.220669	0.588492	10.986433	10.986367	37.223549	802.466304	787.945613	3647.366979	0.807773	-0.087211	-0.581572	-0.033545	0.060515	0.082924	0.187794	3.049213	6.575058	27.655334	219.198164	994.299267	2347.720525	0.802627	-0.089810	-0.588138	-0.041941	0.008288	-0.030010	-0.051537	0.013590	-0.043794	-1.045436	-0.286329	12.855497	2.286077	0.003499	0.001669	0.004241	0.005111	0.019737	0.067714	0.148505	0.890300	2.983282	9.940538	74.094374	227.550948	905.421557
8	7	14	5	warmth	3	4	medium	2	3	2021-08-05 13:22:33.943	2021-08-05 13:22:46.901	0.504132	0.620513	0.675978	1.000000	0.753012	0.652406	0.75625	0.666667	0.497555	0.612646	0.666111	0.497455	0.718864	0.636978	0.748824	0.660607	0.002255	0.002709	0.002836	0.046534	0.004099	0.002556	0.002994	0.002366	0.577869	0.915493	0.809249	0.682540	0.995918	0.873469	0.702970	0.608295	0.496914	0.848668	0.691479	0.634025	0.492229	0.494331	0.594055	0.557547	0.004907	0.006326	0.007682	0.004640	0.056979	0.026719	0.005584	0.003448	-0.755981	-0.051682	0.629959	0.000739	0.001374	0.002246	3.255208	1.613451	2.502441	-0.779094	-0.071283	0.602464	9.041622e-07	5.968817e-07	4.584793e-06	0.001733	0.002841	-0.010751	0.009569	0.010464	0.012897	0.000194	0.000207	0.000319	0.484825	0.448576	0.656571	1.187332	0.063293	0.467117	0.004705	0.002386	0.002492	14.160156	3.987630	5.696615	0.937241	-0.087875	0.348135	-2.234507e-07	1.086783e-05	0.000003	-0.004640	-0.028797	0.015028	0.032818	0.022120	0.074620	0.000564	0.000321	0.000398	1.498355	0.947497	1.130527	0.089504	0.451663	0.094064	-0.885182	0.099985	0.814414	1.598763	6.557470	32.573741	52.519227	1119.885376	2756.053488	19593.461070	0.008720	0.438694	0.043271	-0.896928	-0.002236	0.002943	-0.027811	0.007343	-0.030974	0.190422	1.059380	-5.961497	23.492949	0.027758	0.004081	0.017903	0.003679	0.018631	0.056792	0.189555	0.854680	2.428383	7.714206	99.617406	313.443456	1196.663038	0.836393	-0.054847	-0.520393	-0.037838	0.366688	1.493906	1.229068	32.989582	46.509508	73.605495	2836.091259	5228.533317	7778.819167	0.814328	-0.118854	-0.557526	-0.096314	0.002662	0.002451	-0.016987	-0.041973	-0.008146	-0.305426	-1.126181	-0.480905	-24.261460	0.020973	0.017974	0.032082	0.029029	0.082006	0.267581	0.192136	3.198438	7.364610	9.584111	272.472840	618.347885	1022.027358
9	7	14	6	flexibility	4	1	low	1	1	2021-08-05 13:23:00.627	2021-08-05 13:23:01.621	0.553719	0.907692	0.938548	1.000000	0.939759	0.711230	0.90000	0.738889	0.497474	0.613952	0.666267	0.534995	0.722797	0.637314	0.748474	0.661274	0.016284	0.048497	0.051068	0.175105	0.034314	0.016219	0.018106	0.013654	0.540984	0.908451	0.739884	0.793651	0.497959	0.502041	0.717822	0.640553	0.497050	0.849685	0.692418	0.634612	0.491286	0.493680	0.594688	0.558375	0.010524	0.013761	0.010472	0.019357	0.002313	0.001949	0.033387	0.025487	-0.488403	0.399979	0.732986	0.003457	0.004404	0.001719	15.364583	20.080566	6.901042	-0.688021	0.251760	0.659535	-7.075096e-06	5.670041e-05	-1.309690e-04	0.120762	0.125691	0.153056	0.066806	0.069523	0.033788	0.001710	0.001847	0.001188	5.289589	5.222964	2.715770	1.098953	0.191711	0.534500	0.003911	0.002941	0.002891	17.382812	5.106608	4.699707	0.893211	0.133313	0.439354	-3.621094e-05	3.176758e-05	0.000003	0.094182	-0.240880	0.014179	0.055625	0.034602	0.029598	0.001629	0.000933	0.000620	4.491664	2.375156	1.628285	0.249743	0.415037	-0.027648	-0.875806	0.711375	0.732153	0.678705	42.333498	34.676926	76.460467	4649.299799	2602.846516	4253.812596	0.235366	0.408364	-0.037423	-0.881097	0.002172	0.054805	-0.062035	0.257850	-1.318093	1.169602	-102.321084	-203.355012	156.283713	0.007431	0.004227	0.005104	0.003611	0.332137	0.247243	0.442337	20.231500	16.003703	26.441457	2492.189579	1717.330405	2120.953184	0.713223	-0.215995	-0.467366	-0.461506	0.488642	0.306945	0.288757	20.761490	23.849124	15.865774	4599.641047	2866.425848	1105.649570	0.707679	-0.222934	-0.479116	-0.468872	-0.009047	0.038615	-0.022886	-0.266041	0.622298	-0.175560	31.471239	18.820004	-18.120447	0.003362	0.003920	0.007105	0.004806	0.231302	0.168814	0.119388	9.319012	8.356834	6.251922	907.661263	745.336220	543.331560

	max_ch1_Hand0	max_ch2_Hand0	max_ch3_Hand0	max_ch4_Hand0	max_ch5_Hand0	max_ch6_Hand0	max_ch7_Hand0	max_ch8_Hand0	mean_ch1_Hand0	mean_ch2_Hand0	mean_ch3_Hand0	mean_ch4_Hand0	mean_ch5_Hand0	mean_ch6_Hand0	mean_ch7_Hand0	mean_ch8_Hand0	std_ch1_Hand0	std_ch2_Hand0	std_ch3_Hand0	std_ch4_Hand0	std_ch5_Hand0	std_ch6_Hand0	std_ch7_Hand0	std_ch8_Hand0	max_ch1_Hand1	max_ch2_Hand1	max_ch3_Hand1	max_ch4_Hand1	max_ch5_Hand1	max_ch6_Hand1	max_ch7_Hand1	max_ch8_Hand1	mean_ch1_Hand1	mean_ch2_Hand1	mean_ch3_Hand1	mean_ch4_Hand1	mean_ch5_Hand1	mean_ch6_Hand1	mean_ch7_Hand1	mean_ch8_Hand1	std_ch1_Hand1	std_ch2_Hand1	std_ch3_Hand1	std_ch4_Hand1	std_ch5_Hand1	std_ch6_Hand1	std_ch7_Hand1	std_ch8_Hand1	max_Ax_Hand0	max_Ay_Hand0	max_Az_Hand0	max_Vx_Hand0	max_Vy_Hand0	max_Vz_Hand0	max_Jx_Hand0	max_Jy_Hand0	max_Jz_Hand0	mean_Ax_Hand0	mean_Ay_Hand0	mean_Az_Hand0	mean_Vx_Hand0	mean_Vy_Hand0	mean_Vz_Hand0	mean_Jx_Hand0	mean_Jy_Hand0	mean_Jz_Hand0	std_Ax_Hand0	std_Ay_Hand0	std_Az_Hand0	std_Vx_Hand0	std_Vy_Hand0	std_Vz_Hand0	std_Jx_Hand0	std_Jy_Hand0	std_Jz_Hand0	max_Ax_Hand1	max_Ay_Hand1	max_Az_Hand1	max_Vx_Hand1	max_Vy_Hand1	max_Vz_Hand1	max_Jx_Hand1	max_Jy_Hand1	max_Jz_Hand1	mean_Ax_Hand1	mean_Ay_Hand1	mean_Az_Hand1	mean_Vx_Hand1	mean_Vy_Hand1	mean_Vz_Hand1	mean_Jx_Hand1	mean_Jy_Hand1	mean_Jz_Hand1	std_Ax_Hand1	std_Ay_Hand1	std_Az_Hand1	std_Vx_Hand1	std_Vy_Hand1	std_Vz_Hand1	std_Jx_Hand1	std_Jy_Hand1	std_Jz_Hand1	max_w_Hand0	max_x_Hand0	max_y_Hand0	max_z_Hand0	max_AVx_Hand0	max_AVy_Hand0	max_AVz_Hand0	max_AAx_Hand0	max_AAy_Hand0	max_AAz_Hand0	max_AJx_Hand0	max_AJy_Hand0	max_AJz_Hand0	mean_w_Hand0	mean_x_Hand0	mean_y_Hand0	mean_z_Hand0	mean_AVx_Hand0	mean_AVy_Hand0	mean_AVz_Hand0	mean_AAx_Hand0	mean_AAy_Hand0	mean_AAz_Hand0	mean_AJx_Hand0	mean_AJy_Hand0	mean_AJz_Hand0	std_w_Hand0	std_x_Hand0	std_y_Hand0	std_z_Hand0	std_AVx_Hand0	std_AVy_Hand0	std_AVz_Hand0	std_AAx_Hand0	std_AAy_Hand0	std_AAz_Hand0	std_AJx_Hand0	std_AJy_Hand0	std_AJz_Hand0	max_w_Hand1	max_x_Hand1	max_y_Hand1	max_z_Hand1	max_AVx_Hand1	max_AVy_Hand1	max_AVz_Hand1	max_AAx_Hand1	max_AAy_Hand1	max_AAz_Hand1	max_AJx_Hand1	max_AJy_Hand1	max_AJz_Hand1	mean_w_Hand1	mean_x_Hand1	mean_y_Hand1	mean_z_Hand1	mean_AVx_Hand1	mean_AVy_Hand1	mean_AVz_Hand1	mean_AAx_Hand1	mean_AAy_Hand1	mean_AAz_Hand1	mean_AJx_Hand1	mean_AJy_Hand1	mean_AJz_Hand1	std_w_Hand1	std_x_Hand1	std_y_Hand1	std_z_Hand1	std_AVx_Hand1	std_AVy_Hand1	std_AVz_Hand1	std_AAx_Hand1	std_AAy_Hand1	std_AAz_Hand1	std_AJx_Hand1	std_AJy_Hand1	std_AJz_Hand1
0	0.504132	0.620513	0.675978	0.500000	0.759036	0.652406	0.76250	0.666667	0.497498	0.612780	0.665895	0.492285	0.718526	0.636907	0.748853	0.660720	0.002329	0.002760	0.003011	0.002201	0.006194	0.004289	0.003634	0.002595	0.508197	0.929578	0.757225	0.730159	0.497959	0.995918	0.663366	0.580645	0.497102	0.848495	0.692072	0.634859	0.491574	0.511567	0.596728	0.558018	0.003056	0.009851	0.011220	0.016046	0.002283	0.125342	0.014496	0.005100	-0.850708	-0.068771	0.490311	0.000525	0.000406	0.000903	1.269531	1.220703	2.258301	-0.863979	-0.089162	0.468123	-0.000005	1.483398e-05	-1.191406e-05	-0.010928	-0.052313	0.060087	0.006994	0.008424	0.010178	0.000201	0.000180	0.000270	0.599364	0.630856	0.794158	1.023270	0.042786	-0.178879	0.001218	0.001529	0.001429	4.089355	5.468750	3.271484	0.970242	-0.011443	-0.239064	-0.000030	-8.115234e-06	0.000065	0.061211	0.036287	-0.170119	0.017187	0.014988	0.034359	0.000440	0.000417	0.000641	1.262790	1.374306	1.977670	0.179352	0.488418	0.160615	-0.840857	0.133141	0.061469	0.117029	4.332320	2.887801	19.812834	554.574503	316.450316	1338.775861	0.171550	0.486515	0.155002	-0.842509	-0.002358	-0.006487	-0.037808	-0.069210	0.040128	0.213198	-18.896953	-5.997847	29.264918	0.004033	0.001122	0.003172	0.000812	0.034035	0.027288	0.100030	1.808784	1.349269	6.515867	214.502864	135.316892	572.754308	0.618680	-0.175326	-0.753378	-0.128901	0.215491	0.583907	0.275227	21.603607	56.450019	29.424453	2797.763022	9050.317251	2366.953044	0.602964	-0.177295	-0.766545	-0.131596	-0.011132	-0.035192	-0.000290	-0.692534	-0.994509	-0.440578	19.388504	94.341574	-56.539654	0.007223	0.001357	0.006089	0.001773	0.140168	0.419609	0.109731	8.409602	20.970763	7.985693	769.853603	1878.284384	898.072858
1	0.504132	0.620513	0.675978	0.500000	0.759036	0.663102	0.75625	0.666667	0.497417	0.612789	0.666077	0.492186	0.718908	0.637194	0.748828	0.660428	0.002321	0.002785	0.002810	0.002245	0.006368	0.004426	0.002994	0.002613	0.508197	0.957746	0.809249	0.746032	0.497959	0.995918	0.683168	0.585254	0.496918	0.848890	0.692686	0.634612	0.491212	0.492734	0.596994	0.558059	0.003603	0.012522	0.017839	0.015241	0.002220	0.168270	0.014712	0.007247	-0.846313	-0.069260	0.488846	0.000545	0.000305	0.000813	2.929688	1.190186	3.580729	-0.863563	-0.084855	0.464930	-0.000007	2.042809e-06	-2.849968e-06	0.048335	-0.042143	0.040023	0.006822	0.005053	0.009881	0.000240	0.000117	0.000297	0.712536	0.499915	0.939672	0.999344	0.062317	-0.159836	0.001187	0.001328	0.001074	3.255208	3.320312	4.882812	0.965975	-0.000925	-0.259182	0.000032	6.406250e-05	-0.000024	-0.095776	-0.165980	0.144850	0.015653	0.019206	0.046904	0.000333	0.000457	0.000460	1.187168	1.426576	1.346959	0.164387	0.492346	0.149139	-0.843806	0.051026	0.074743	0.071737	8.423988	6.857490	10.689540	2084.369900	445.379626	810.506826	0.160231	0.490322	0.145928	-0.844157	-0.003892	-0.003804	-0.018201	-0.036467	0.077885	-0.006631	26.988689	-7.739067	-51.168052	0.002767	0.001331	0.002246	0.000222	0.031651	0.021172	0.067094	1.891410	1.482885	4.074346	324.990077	135.612823	500.564267	0.618863	-0.150822	-0.754349	-0.114060	0.383834	0.784153	0.553013	11.380460	29.010612	33.358820	2752.365093	6546.331906	2181.850987	0.596188	-0.163353	-0.775710	-0.124714	0.021907	0.013755	0.012748	-1.111580	-0.334962	0.021466	-90.668628	-64.601575	35.073660	0.015482	0.009833	0.014861	0.007212	0.166403	0.417988	0.173217	7.359519	14.858287	9.153837	744.182656	1454.186126	769.701026
2	0.685950	0.728205	0.849162	0.607438	0.777108	0.700535	0.87500	0.733333	0.497469	0.612832	0.666206	0.492255	0.718535	0.637126	0.748788	0.660685	0.011144	0.006879	0.009090	0.007845	0.005614	0.004051	0.005506	0.004440	0.516393	0.936620	0.832370	0.751323	0.995918	0.995918	0.698020	0.622120	0.496948	0.848779	0.691786	0.634099	0.486646	0.498963	0.595900	0.557786	0.003032	0.007777	0.009667	0.009077	0.043924	0.108942	0.010389	0.006009	-0.713501	0.048904	0.572830	0.006123	0.002393	0.002616	15.869141	6.692325	6.749770	-0.868275	-0.069352	0.459729	-0.000030	-6.790516e-07	-3.816164e-06	0.042151	0.006334	-0.003044	0.024366	0.017504	0.021913	0.000905	0.000376	0.000411	1.608533	0.921114	1.017115	1.060379	0.134583	-0.014328	0.001955	0.002280	0.005502	11.568510	13.627486	7.359096	0.978988	-0.000388	-0.202378	0.000005	2.112100e-07	-0.000013	-0.005919	-0.001242	0.023884	0.015082	0.015613	0.041390	0.000334	0.000404	0.000483	1.085616	1.194701	1.336285	0.168521	0.505227	0.142912	-0.835808	0.316624	0.593621	0.331014	19.806659	22.033338	26.051293	2206.180328	2613.281789	2713.864594	0.155634	0.496838	0.135235	-0.842923	-0.001639	-0.004225	0.000291	-0.003520	-0.114047	0.050148	1.585695	-0.670795	3.401131	0.005331	0.007307	0.004144	0.004806	0.054181	0.088889	0.070436	2.939673	3.487398	4.355037	268.202026	277.300592	448.167800	0.647831	-0.136257	-0.732800	-0.102524	0.879971	1.989502	1.122907	49.389847	85.475258	61.100255	4256.775521	8723.328584	4294.956003	0.619735	-0.153120	-0.759202	-0.124798	0.001542	0.004674	-0.003122	0.079193	0.174868	0.141086	4.852312	18.350814	2.436863	0.012631	0.008083	0.012225	0.012151	0.123687	0.282669	0.113030	4.806996	10.779876	6.534156	486.415887	1142.095742	625.153662
3	0.512397	0.661538	0.754190	0.512397	0.759036	0.673797	0.76250	0.677778	0.497326	0.612893	0.666100	0.484996	0.718373	0.637000	0.748664	0.660854	0.004125	0.014409	0.021173	0.058099	0.007076	0.005177	0.005281	0.004803	0.516393	0.873239	0.716763	0.666667	0.493878	0.497959	0.643564	0.580645	0.496862	0.848702	0.691813	0.634084	0.491064	0.493647	0.593827	0.557622	0.003913	0.006136	0.005968	0.004635	0.002087	0.001368	0.008473	0.005300	-0.724731	0.207108	0.604568	0.001240	0.002000	0.000923	6.787109	3.352865	2.838135	-0.789386	0.166772	0.569221	-0.000017	5.648744e-06	6.577436e-06	0.128443	-0.119382	0.047230	0.021868	0.015788	0.017773	0.000546	0.000486	0.000461	1.863614	1.430691	1.313220	1.033524	0.126770	0.421707	0.001523	0.000680	0.001583	5.789621	3.756010	5.208333	0.939998	0.078811	0.345779	-0.000055	-9.718750e-05	-0.000015	0.111309	0.174966	-0.011341	0.032360	0.014418	0.032139	0.000701	0.000572	0.000436	1.909429	1.065716	1.424733	0.211566	0.465286	0.013511	-0.859402	0.093546	0.263660	0.136374	10.712655	24.969117	24.077240	2666.816885	1920.723426	5029.040161	0.205977	0.460094	0.012212	-0.863552	0.000240	0.012612	-0.032937	0.277966	0.539358	-0.114937	39.024051	-14.919398	87.718777	0.002371	0.002726	0.000789	0.001891	0.062111	0.086016	0.096223	3.429911	6.023802	6.526026	441.445219	477.288587	875.383706	0.733975	-0.215858	-0.515914	-0.370804	0.271520	0.413471	0.793342	29.883033	33.672845	86.168465	3965.979471	4675.769180	11733.432855	0.726422	-0.222149	-0.527491	-0.380252	-0.081652	-0.042828	0.027246	-1.330705	-1.340691	1.024436	-49.463745	-93.159203	-58.539333	0.005213	0.004116	0.008078	0.003958	0.198462	0.183171	0.155875	11.152448	9.963270	16.088213	1150.663729	1135.642103	2119.655762
4	0.504132	0.635897	0.737430	1.000000	0.746988	0.652406	0.76250	0.672222	0.497305	0.612760	0.666103	0.539813	0.720609	0.636990	0.748779	0.661079	0.003440	0.007323	0.011248	0.095872	0.007634	0.003822	0.004761	0.004264	0.512295	0.866197	0.710983	0.650794	0.497959	0.497959	0.722772	0.599078	0.496943	0.848932	0.691707	0.633865	0.491129	0.493722	0.594159	0.558040	0.003295	0.004746	0.003927	0.004114	0.002148	0.001221	0.017247	0.007301	-0.774048	0.188553	0.582108	0.000586	0.000533	0.000714	2.859933	1.497396	2.392578	-0.796210	0.169789	0.556567	-0.000016	1.522640e-05	-6.975446e-08	0.037976	-0.004908	0.032503	0.010191	0.008518	0.010531	0.000297	0.000196	0.000290	0.819147	0.545627	0.845935	1.021317	0.099915	0.340652	0.002075	0.001179	0.000612	3.200955	1.819957	1.864347	0.956346	0.070920	0.300213	0.000043	9.736328e-06	-0.000034	-0.036841	-0.036219	0.075440	0.021201	0.012754	0.029698	0.000486	0.000285	0.000374	1.348408	0.832293	0.830523	0.202624	0.467448	0.012214	-0.860776	0.052285	0.040986	0.116178	2.856049	3.770583	15.126930	312.982165	281.025841	1062.566337	0.200699	0.466227	0.007997	-0.861557	-0.006016	-0.012182	-0.007998	0.021963	0.064504	0.191972	7.439429	3.549816	8.698628	0.001243	0.001357	0.001932	0.000563	0.026385	0.029268	0.078408	1.107083	1.317382	4.622568	85.712120	106.001201	406.024850	0.722931	-0.229336	-0.534479	-0.366098	0.563110	0.565189	0.085749	12.485845	20.251992	7.395605	1233.738922	2111.723609	964.527774	0.715442	-0.232278	-0.544587	-0.370770	0.045293	0.053730	-0.001529	-0.013939	0.110153	-0.246766	-31.768089	-36.879092	-7.538979	0.006682	0.002770	0.009360	0.002573	0.181719	0.217952	0.053026	5.253641	7.297097	3.331552	548.393076	819.998059	375.285448

	participant_id	clothes_id	property_id	property_name	interaction_id	rating	rating_level	rating_level_num	sub_window_num	subwindow_start_time	subwindow_end_time	max_ch1_Hand0	max_ch2_Hand0	max_ch3_Hand0	max_ch4_Hand0	max_ch5_Hand0	max_ch6_Hand0	max_ch7_Hand0	max_ch8_Hand0	mean_ch1_Hand0	mean_ch2_Hand0	mean_ch3_Hand0	mean_ch4_Hand0	mean_ch5_Hand0	mean_ch6_Hand0	mean_ch7_Hand0	mean_ch8_Hand0	std_ch1_Hand0	std_ch2_Hand0	std_ch3_Hand0	std_ch4_Hand0	std_ch5_Hand0	std_ch6_Hand0	std_ch7_Hand0	std_ch8_Hand0	max_ch1_Hand1	max_ch2_Hand1	max_ch3_Hand1	max_ch4_Hand1	max_ch5_Hand1	max_ch6_Hand1	max_ch7_Hand1	max_ch8_Hand1	mean_ch1_Hand1	mean_ch2_Hand1	mean_ch3_Hand1	mean_ch4_Hand1	mean_ch5_Hand1	mean_ch6_Hand1	mean_ch7_Hand1	mean_ch8_Hand1	std_ch1_Hand1	std_ch2_Hand1	std_ch3_Hand1	std_ch4_Hand1	std_ch5_Hand1	std_ch6_Hand1	std_ch7_Hand1	std_ch8_Hand1	max_Ax_Hand0	max_Ay_Hand0	max_Az_Hand0	max_Vx_Hand0	max_Vy_Hand0	max_Vz_Hand0	max_Jx_Hand0	max_Jy_Hand0	max_Jz_Hand0	mean_Ax_Hand0	mean_Ay_Hand0	mean_Az_Hand0	mean_Vx_Hand0	mean_Vy_Hand0	mean_Vz_Hand0	mean_Jx_Hand0	mean_Jy_Hand0	mean_Jz_Hand0	std_Ax_Hand0	std_Ay_Hand0	std_Az_Hand0	std_Vx_Hand0	std_Vy_Hand0	std_Vz_Hand0	std_Jx_Hand0	std_Jy_Hand0	std_Jz_Hand0	max_Ax_Hand1	max_Ay_Hand1	max_Az_Hand1	max_Vx_Hand1	max_Vy_Hand1	max_Vz_Hand1	max_Jx_Hand1	max_Jy_Hand1	max_Jz_Hand1	mean_Ax_Hand1	mean_Ay_Hand1	mean_Az_Hand1	mean_Vx_Hand1	mean_Vy_Hand1	mean_Vz_Hand1	mean_Jx_Hand1	mean_Jy_Hand1	mean_Jz_Hand1	std_Ax_Hand1	std_Ay_Hand1	std_Az_Hand1	std_Vx_Hand1	std_Vy_Hand1	std_Vz_Hand1	std_Jx_Hand1	std_Jy_Hand1	std_Jz_Hand1	max_w_Hand0	max_x_Hand0	max_y_Hand0	max_z_Hand0	max_AVx_Hand0	max_AVy_Hand0	max_AVz_Hand0	max_AAx_Hand0	max_AAy_Hand0	max_AAz_Hand0	max_AJx_Hand0	max_AJy_Hand0	max_AJz_Hand0	mean_w_Hand0	mean_x_Hand0	mean_y_Hand0	mean_z_Hand0	mean_AVx_Hand0	mean_AVy_Hand0	mean_AVz_Hand0	mean_AAx_Hand0	mean_AAy_Hand0	mean_AAz_Hand0	mean_AJx_Hand0	mean_AJy_Hand0	mean_AJz_Hand0	std_w_Hand0	std_x_Hand0	std_y_Hand0	std_z_Hand0	std_AVx_Hand0	std_AVy_Hand0	std_AVz_Hand0	std_AAx_Hand0	std_AAy_Hand0	std_AAz_Hand0	std_AJx_Hand0	std_AJy_Hand0	std_AJz_Hand0	max_w_Hand1	max_x_Hand1	max_y_Hand1	max_z_Hand1	max_AVx_Hand1	max_AVy_Hand1	max_AVz_Hand1	max_AAx_Hand1	max_AAy_Hand1	max_AAz_Hand1	max_AJx_Hand1	max_AJy_Hand1	max_AJz_Hand1	mean_w_Hand1	mean_x_Hand1	mean_y_Hand1	mean_z_Hand1	mean_AVx_Hand1	mean_AVy_Hand1	mean_AVz_Hand1	mean_AAx_Hand1	mean_AAy_Hand1	mean_AAz_Hand1	mean_AJx_Hand1	mean_AJy_Hand1	mean_AJz_Hand1	std_w_Hand1	std_x_Hand1	std_y_Hand1	std_z_Hand1	std_AVx_Hand1	std_AVy_Hand1	std_AVz_Hand1	std_AAx_Hand1	std_AAy_Hand1	std_AAz_Hand1	std_AJx_Hand1	std_AJy_Hand1	std_AJz_Hand1
0	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.504132	0.620513	0.675978	0.500000	0.759036	0.652406	0.76250	0.666667	0.497498	0.612780	0.665895	0.492285	0.718526	0.636907	0.748853	0.660720	0.002329	0.002760	0.003011	0.002201	0.006194	0.004289	0.003634	0.002595	0.508197	0.929578	0.757225	0.730159	0.497959	0.995918	0.663366	0.580645	0.497102	0.848495	0.692072	0.634859	0.491574	0.511567	0.596728	0.558018	0.003056	0.009851	0.011220	0.016046	0.002283	0.125342	0.014496	0.005100	-0.850708	-0.068771	0.490311	0.000525	0.000406	0.000903	1.269531	1.220703	2.258301	-0.863979	-0.089162	0.468123	-0.000005	1.483398e-05	-1.191406e-05	-0.010928	-0.052313	0.060087	0.006994	0.008424	0.010178	0.000201	0.000180	0.000270	0.599364	0.630856	0.794158	1.023270	0.042786	-0.178879	0.001218	0.001529	0.001429	4.089355	5.468750	3.271484	0.970242	-0.011443	-0.239064	-0.000030	-8.115234e-06	0.000065	0.061211	0.036287	-0.170119	0.017187	0.014988	0.034359	0.000440	0.000417	0.000641	1.262790	1.374306	1.977670	0.179352	0.488418	0.160615	-0.840857	0.133141	0.061469	0.117029	4.332320	2.887801	19.812834	554.574503	316.450316	1338.775861	0.171550	0.486515	0.155002	-0.842509	-0.002358	-0.006487	-0.037808	-0.069210	0.040128	0.213198	-18.896953	-5.997847	29.264918	0.004033	0.001122	0.003172	0.000812	0.034035	0.027288	0.100030	1.808784	1.349269	6.515867	214.502864	135.316892	572.754308	0.618680	-0.175326	-0.753378	-0.128901	0.215491	0.583907	0.275227	21.603607	56.450019	29.424453	2797.763022	9050.317251	2366.953044	0.602964	-0.177295	-0.766545	-0.131596	-0.011132	-0.035192	-0.000290	-0.692534	-0.994509	-0.440578	19.388504	94.341574	-56.539654	0.007223	0.001357	0.006089	0.001773	0.140168	0.419609	0.109731	8.409602	20.970763	7.985693	769.853603	1878.284384	898.072858
1	7	14	3	smoothness	1	3	medium	2	2	2021-08-05 13:21:32.495	2021-08-05 13:21:33.464	0.504132	0.620513	0.675978	0.500000	0.759036	0.663102	0.75625	0.666667	0.497417	0.612789	0.666077	0.492186	0.718908	0.637194	0.748828	0.660428	0.002321	0.002785	0.002810	0.002245	0.006368	0.004426	0.002994	0.002613	0.508197	0.957746	0.809249	0.746032	0.497959	0.995918	0.683168	0.585254	0.496918	0.848890	0.692686	0.634612	0.491212	0.492734	0.596994	0.558059	0.003603	0.012522	0.017839	0.015241	0.002220	0.168270	0.014712	0.007247	-0.846313	-0.069260	0.488846	0.000545	0.000305	0.000813	2.929688	1.190186	3.580729	-0.863563	-0.084855	0.464930	-0.000007	2.042809e-06	-2.849968e-06	0.048335	-0.042143	0.040023	0.006822	0.005053	0.009881	0.000240	0.000117	0.000297	0.712536	0.499915	0.939672	0.999344	0.062317	-0.159836	0.001187	0.001328	0.001074	3.255208	3.320312	4.882812	0.965975	-0.000925	-0.259182	0.000032	6.406250e-05	-0.000024	-0.095776	-0.165980	0.144850	0.015653	0.019206	0.046904	0.000333	0.000457	0.000460	1.187168	1.426576	1.346959	0.164387	0.492346	0.149139	-0.843806	0.051026	0.074743	0.071737	8.423988	6.857490	10.689540	2084.369900	445.379626	810.506826	0.160231	0.490322	0.145928	-0.844157	-0.003892	-0.003804	-0.018201	-0.036467	0.077885	-0.006631	26.988689	-7.739067	-51.168052	0.002767	0.001331	0.002246	0.000222	0.031651	0.021172	0.067094	1.891410	1.482885	4.074346	324.990077	135.612823	500.564267	0.618863	-0.150822	-0.754349	-0.114060	0.383834	0.784153	0.553013	11.380460	29.010612	33.358820	2752.365093	6546.331906	2181.850987	0.596188	-0.163353	-0.775710	-0.124714	0.021907	0.013755	0.012748	-1.111580	-0.334962	0.021466	-90.668628	-64.601575	35.073660	0.015482	0.009833	0.014861	0.007212	0.166403	0.417988	0.173217	7.359519	14.858287	9.153837	744.182656	1454.186126	769.701026
2	7	14	3	smoothness	1	3	medium	2	3	2021-08-05 13:21:33.513	2021-08-05 13:21:46.475	0.685950	0.728205	0.849162	0.607438	0.777108	0.700535	0.87500	0.733333	0.497469	0.612832	0.666206	0.492255	0.718535	0.637126	0.748788	0.660685	0.011144	0.006879	0.009090	0.007845	0.005614	0.004051	0.005506	0.004440	0.516393	0.936620	0.832370	0.751323	0.995918	0.995918	0.698020	0.622120	0.496948	0.848779	0.691786	0.634099	0.486646	0.498963	0.595900	0.557786	0.003032	0.007777	0.009667	0.009077	0.043924	0.108942	0.010389	0.006009	-0.713501	0.048904	0.572830	0.006123	0.002393	0.002616	15.869141	6.692325	6.749770	-0.868275	-0.069352	0.459729	-0.000030	-6.790516e-07	-3.816164e-06	0.042151	0.006334	-0.003044	0.024366	0.017504	0.021913	0.000905	0.000376	0.000411	1.608533	0.921114	1.017115	1.060379	0.134583	-0.014328	0.001955	0.002280	0.005502	11.568510	13.627486	7.359096	0.978988	-0.000388	-0.202378	0.000005	2.112100e-07	-0.000013	-0.005919	-0.001242	0.023884	0.015082	0.015613	0.041390	0.000334	0.000404	0.000483	1.085616	1.194701	1.336285	0.168521	0.505227	0.142912	-0.835808	0.316624	0.593621	0.331014	19.806659	22.033338	26.051293	2206.180328	2613.281789	2713.864594	0.155634	0.496838	0.135235	-0.842923	-0.001639	-0.004225	0.000291	-0.003520	-0.114047	0.050148	1.585695	-0.670795	3.401131	0.005331	0.007307	0.004144	0.004806	0.054181	0.088889	0.070436	2.939673	3.487398	4.355037	268.202026	277.300592	448.167800	0.647831	-0.136257	-0.732800	-0.102524	0.879971	1.989502	1.122907	49.389847	85.475258	61.100255	4256.775521	8723.328584	4294.956003	0.619735	-0.153120	-0.759202	-0.124798	0.001542	0.004674	-0.003122	0.079193	0.174868	0.141086	4.852312	18.350814	2.436863	0.012631	0.008083	0.012225	0.012151	0.123687	0.282669	0.113030	4.806996	10.779876	6.534156	486.415887	1142.095742	625.153662
3	7	14	4	thickness	2	5	medium	2	1	2021-08-05 13:22:02.060	2021-08-05 13:22:03.029	0.512397	0.661538	0.754190	0.512397	0.759036	0.673797	0.76250	0.677778	0.497326	0.612893	0.666100	0.484996	0.718373	0.637000	0.748664	0.660854	0.004125	0.014409	0.021173	0.058099	0.007076	0.005177	0.005281	0.004803	0.516393	0.873239	0.716763	0.666667	0.493878	0.497959	0.643564	0.580645	0.496862	0.848702	0.691813	0.634084	0.491064	0.493647	0.593827	0.557622	0.003913	0.006136	0.005968	0.004635	0.002087	0.001368	0.008473	0.005300	-0.724731	0.207108	0.604568	0.001240	0.002000	0.000923	6.787109	3.352865	2.838135	-0.789386	0.166772	0.569221	-0.000017	5.648744e-06	6.577436e-06	0.128443	-0.119382	0.047230	0.021868	0.015788	0.017773	0.000546	0.000486	0.000461	1.863614	1.430691	1.313220	1.033524	0.126770	0.421707	0.001523	0.000680	0.001583	5.789621	3.756010	5.208333	0.939998	0.078811	0.345779	-0.000055	-9.718750e-05	-0.000015	0.111309	0.174966	-0.011341	0.032360	0.014418	0.032139	0.000701	0.000572	0.000436	1.909429	1.065716	1.424733	0.211566	0.465286	0.013511	-0.859402	0.093546	0.263660	0.136374	10.712655	24.969117	24.077240	2666.816885	1920.723426	5029.040161	0.205977	0.460094	0.012212	-0.863552	0.000240	0.012612	-0.032937	0.277966	0.539358	-0.114937	39.024051	-14.919398	87.718777	0.002371	0.002726	0.000789	0.001891	0.062111	0.086016	0.096223	3.429911	6.023802	6.526026	441.445219	477.288587	875.383706	0.733975	-0.215858	-0.515914	-0.370804	0.271520	0.413471	0.793342	29.883033	33.672845	86.168465	3965.979471	4675.769180	11733.432855	0.726422	-0.222149	-0.527491	-0.380252	-0.081652	-0.042828	0.027246	-1.330705	-1.340691	1.024436	-49.463745	-93.159203	-58.539333	0.005213	0.004116	0.008078	0.003958	0.198462	0.183171	0.155875	11.152448	9.963270	16.088213	1150.663729	1135.642103	2119.655762
4	7	14	4	thickness	2	5	medium	2	2	2021-08-05 13:22:03.061	2021-08-05 13:22:04.047	0.504132	0.635897	0.737430	1.000000	0.746988	0.652406	0.76250	0.672222	0.497305	0.612760	0.666103	0.539813	0.720609	0.636990	0.748779	0.661079	0.003440	0.007323	0.011248	0.095872	0.007634	0.003822	0.004761	0.004264	0.512295	0.866197	0.710983	0.650794	0.497959	0.497959	0.722772	0.599078	0.496943	0.848932	0.691707	0.633865	0.491129	0.493722	0.594159	0.558040	0.003295	0.004746	0.003927	0.004114	0.002148	0.001221	0.017247	0.007301	-0.774048	0.188553	0.582108	0.000586	0.000533	0.000714	2.859933	1.497396	2.392578	-0.796210	0.169789	0.556567	-0.000016	1.522640e-05	-6.975446e-08	0.037976	-0.004908	0.032503	0.010191	0.008518	0.010531	0.000297	0.000196	0.000290	0.819147	0.545627	0.845935	1.021317	0.099915	0.340652	0.002075	0.001179	0.000612	3.200955	1.819957	1.864347	0.956346	0.070920	0.300213	0.000043	9.736328e-06	-0.000034	-0.036841	-0.036219	0.075440	0.021201	0.012754	0.029698	0.000486	0.000285	0.000374	1.348408	0.832293	0.830523	0.202624	0.467448	0.012214	-0.860776	0.052285	0.040986	0.116178	2.856049	3.770583	15.126930	312.982165	281.025841	1062.566337	0.200699	0.466227	0.007997	-0.861557	-0.006016	-0.012182	-0.007998	0.021963	0.064504	0.191972	7.439429	3.549816	8.698628	0.001243	0.001357	0.001932	0.000563	0.026385	0.029268	0.078408	1.107083	1.317382	4.622568	85.712120	106.001201	406.024850	0.722931	-0.229336	-0.534479	-0.366098	0.563110	0.565189	0.085749	12.485845	20.251992	7.395605	1233.738922	2111.723609	964.527774	0.715442	-0.232278	-0.544587	-0.370770	0.045293	0.053730	-0.001529	-0.013939	0.110153	-0.246766	-31.768089	-36.879092	-7.538979	0.006682	0.002770	0.009360	0.002573	0.181719	0.217952	0.053026	5.253641	7.297097	3.331552	548.393076	819.998059	375.285448

	participant_id	clothes_id	property_id	property_name	interaction_id	rating	rating_level	rating_level_num	sub_window_num	subwindow_start_time	subwindow_end_time	mean_ch1	mean_ch2	mean_ch3	mean_ch4	mean_ch5	mean_ch6	mean_ch7	mean_ch8
0	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497676	0.613782	0.665852	0.491478	0.719126	0.635695	0.748047	0.661458
1	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497676	0.612500	0.666201	0.492511	0.719126	0.636030	0.748828	0.661458
2	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497934	0.613782	0.665503	0.492252	0.718750	0.636698	0.749609	0.659722
3	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497934	0.613782	0.665502	0.492511	0.717620	0.636364	0.749219	0.660764
4	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497676	0.612500	0.666201	0.493027	0.715738	0.634693	0.748828	0.661806
5	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.496643	0.612179	0.666201	0.492252	0.719880	0.637366	0.749609	0.661806
6	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497676	0.613141	0.664804	0.492511	0.717620	0.635361	0.750391	0.660764
7	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497934	0.612820	0.665852	0.493027	0.717620	0.637366	0.748047	0.661806
8	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.497676	0.613462	0.666201	0.492252	0.717997	0.637032	0.748828	0.661111
9	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.481	2021-08-05 13:21:32.468	0.496901	0.611538	0.666550	0.492252	0.719503	0.636030	0.748047	0.660069

	participant_id	clothes_id	property_id	property_name	interaction_id	rating	rating_level	rating_level_num	sub_window_num	subwindow_start_time	subwindow_end_time	mean_ch1	mean_ch2	mean_ch3	mean_ch4	mean_ch5	mean_ch6	mean_ch7	mean_ch8
0	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.496926	0.847271	0.693642	0.638228	0.491072	0.485714	0.593750	0.559332
1	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.496414	0.844630	0.689306	0.638228	0.491582	0.525000	0.599319	0.559620
2	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.496670	0.848591	0.691835	0.632275	0.491582	0.815306	0.611077	0.557604
3	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.497439	0.850792	0.692558	0.635252	0.492347	0.544388	0.599629	0.558468
4	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.496926	0.849472	0.695809	0.636574	0.492347	0.476786	0.594678	0.557028
5	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.497439	0.848591	0.690390	0.635252	0.491072	0.479592	0.594988	0.557316
6	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.497695	0.849472	0.691113	0.634590	0.492092	0.514541	0.594988	0.557604
7	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.497439	0.846831	0.691474	0.634590	0.491327	0.486225	0.594988	0.557316
8	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.497183	0.847711	0.691113	0.634590	0.491582	0.489286	0.596225	0.558180
9	7	14	3	smoothness	1	3	medium	2	1	2021-08-05 13:21:31.489	2021-08-05 13:21:32.480	0.497183	0.848591	0.692197	0.632937	0.491072	0.452806	0.593440	0.558468

Neural Network models - Leave One Participant Out CV to predict properties using 3 subwindows¶

Load data¶

3 window combined df¶

15 window emg¶

Normalise the data¶

3 window combined df¶

15 window emg¶

Create X and y data¶

Model 1 - Linear model using all 180 features¶

Create the model¶

Run the model for properties¶

Run the model for ratings¶

Model 2 - Linear model using only the emg data (48 features)¶

Create the model¶

Run the model for properties¶

Run the model for ratings¶

Model 3 - LSTM + fully connected layers using all 180 features¶

Create the model¶

Run the model for properties¶

Run the model for ratings¶

Model 4 - LSTM + fully connected layers using only the emg data (48 features)¶

Create the model¶

Run the model¶

Run the model for ratings¶

Model 5 - CNN + LSTM¶

Create the model¶

Run the model¶